def search(self, search_space, search_iter, n_estimators, x, y):
if 'n_estimators' in search_space:
del search_space['n_estimators']
params = {
'boosting_type': ['gbdt'],
'min_child_weight': [5],
'min_split_gain': [1.0],
'subsample': [0.8],
'colsample_bytree': [0.6],
'max_depth': [10],
'n_estimators': n_estimators,
'num_leaves': [70],
'learning_rate': [0.04],
}
params.update(search_space)
if self.verbose:
print(params)
folds = 3
score_metric, skf = self.get_skf(folds)
random_search = RandomizedSearchCV(self.lgbm, param_distributions=params, n_iter=search_iter,
scoring=score_metric,
n_jobs=1, cv=skf, verbose=0, random_state=1001)
random_search.fit(x, y)
self.clf = random_search.best_estimator_
return random_search.best_params_
def parameter_search(model, X, y, params, metric, n=10):
'''
returns the best parameters of the classification model
'''
random_search = RandomizedSearchCV(model, param_distributions=params, \
scoring = metric, n_jobs=3, n_iter=n)
random_search.fit(X, y)
return random_search
def pr_curve(i):
label = labels[i]
statistics_l = Statistics()
print('Doing label {}'.format(label))
for train_idx, valid_idx in folds:
rng = np.random.RandomState()
rng.seed(seeds[i])
training_fold = developement_df.loc[train_idx, ]
training_fold = training_fold.reset_index(drop=True)
validation_fold = developement_df.loc[valid_idx, ]
validation_fold = validation_fold.reset_index(drop=True)
base_estimators = make_classifiers(method, balanced, labels, random_state=rng)
# Find the best params, then do a final proper calibration.
base_estimator = base_estimators[label]
estimator = RandomizedSearchCV(
estimator=base_estimator, param_distributions=params,
n_iter=60, scoring='f1', cv=3, random_state=rng,
error_score=0.0, n_jobs=1, pre_dispatch='2*n_jobs',
refit=True
)
# Set up the vectorizer for the bag-of-words representation
if vectorizer_method == 'tf-idf':
vectorizer = TfidfVectorizer(
stop_words=['go', '', ' '], binary=binary, lowercase=True,
sublinear_tf=False, max_df=1.0, min_df=0
)
vectorizer.fit(training_fold['terms'].values)
elif vectorizer_method == 'count':
vectorizer = CountVectorizer(
stop_words=['go', '', ' '], binary=binary, lowercase=True
)
vectorizer.fit(training_fold['terms'].values)
# Fit an evaluate the performance of the classifier.
x_train = vectorizer.transform(training_fold['terms'].values)
y_train = np.asarray(training_fold[label].values, dtype=int)
x_valid = vectorizer.transform(validation_fold['terms'].values)
y_valid = np.asarray(validation_fold[label].values, dtype=int)
estimator.fit(x_train, y_train)
for t in thresholds:
y_pred = [int(p[1] >= t) for p in estimator.predict_proba(x_valid)]
precision = precision_score(y_valid, y_pred, labels=[0, 1], pos_label=1)
recall = recall_score(y_valid, y_pred, labels=[0, 1], pos_label=1)
f1 = f1_score(y_valid, y_pred, labels=[0, 1], pos_label=1)
statistics_l.update_statistics(label=t, s_type='Precision', data=precision)
statistics_l.update_statistics(label=t, s_type='Recall', data=recall)
statistics_l.update_statistics(label=t, s_type='F1-Score', data=f1)
statistics_l.frame()['reaction'] = label
return statistics_l
def build_nn(x_train, y_train, x_test, y_test, n_features):
"""
Constructing a regression neural network model from input dataframe
:param x_train: features dataframe for model training
:param y_train: target dataframe for model training
:param x_test: features dataframe for model testing
:param y_test: target dataframe for model testing
:return: None
"""
net = NeuralNet(layers=[('input', InputLayer),
('hidden0', DenseLayer),
('hidden1', DenseLayer),
('output', DenseLayer)],
input_shape=(None, x_train.shape[1]), # Number of i/p nodes = number of columns in x
hidden0_num_units=15,
hidden0_nonlinearity=lasagne.nonlinearities.softmax,
hidden1_num_units=17,
hidden1_nonlinearity=lasagne.nonlinearities.softmax,
output_num_units=1, # Number of o/p nodes = number of columns in y
output_nonlinearity=lasagne.nonlinearities.softmax,
max_epochs=100,
update_learning_rate=0.01,
regression=True,
verbose=0)
# Finding the optimal set of params for each variable in the training of the neural network
param_dist = {'hidden0_num_units':sp_randint(3, 30), 'hidden1_num_units':sp_randint(3, 30)}
clf = RandomizedSearchCV(estimator=net, param_distributions=param_dist,
n_iter=15, n_jobs=-1)
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
# Mean absolute error regression loss
mean_abs = sklearn.metrics.mean_absolute_error(y_test, y_pred)
# Mean squared error regression loss
mean_sq = sklearn.metrics.mean_squared_error(y_test, y_pred)
# Median absolute error regression loss
median_abs = sklearn.metrics.median_absolute_error(y_test, y_pred)
# R^2 (coefficient of determination) regression score function
r2 = sklearn.metrics.r2_score(y_test, y_pred)
# Explained variance regression score function
exp_var_score = sklearn.metrics.explained_variance_score(y_test, y_pred)
with open('../trained_networks/nn_%d_data.pkl' % n_features, 'wb') as results:
pickle.dump(clf, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(net, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(mean_abs, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(mean_sq, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(median_abs, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(r2, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(exp_var_score, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(y_pred, results, pickle.HIGHEST_PROTOCOL)
return
def test_trivial_cv_results_attr():
# Test search over a "grid" with only one point.
# Non-regression test: grid_scores_ wouldn't be set by GridSearchCV.
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1]})
grid_search.fit(X, y)
assert_true(hasattr(grid_search, "cv_results_"))
random_search = RandomizedSearchCV(clf, {'foo_param': [0]}, n_iter=1)
random_search.fit(X, y)
assert_true(hasattr(grid_search, "cv_results_"))
def test_pickle():
# Test that a fit search can be pickled
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True)
grid_search.fit(X, y)
pickle.dumps(grid_search) # smoke test
random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]},
refit=True, n_iter=3)
random_search.fit(X, y)
pickle.dumps(random_search) # smoke test
def test_randomgridsearch_slm(make_gaus_data):
X, y, Xs, ys = make_gaus_data
slm = StandardLinearModel(LinearBasis(onescol=True))
param_dict = {
'var': [Parameter(1.0 / v, Positive()) for v in range(1, 6)]
}
estimator = RandomizedSearchCV(slm, param_dict, n_jobs=-1, n_iter=2)
estimator.fit(X, y)
Ey = estimator.predict(Xs)
assert len(ys) == len(Ey) # we just want to make sure this all runs
def test_randomgridsearch_glm(make_gaus_data):
X, y, Xs, ys = make_gaus_data
glm = GeneralizedLinearModel(Gaussian(), LinearBasis(onescol=True),
random_state=1, maxiter=100)
param_dict = {'batch_size': range(1, 11)}
estimator = RandomizedSearchCV(glm, param_dict, verbose=1, n_jobs=-1,
n_iter=2)
estimator.fit(X, y)
Ey = estimator.predict(Xs)
assert len(ys) == len(Ey) # we just want to make sure this all runs
def test_pickle():
# Test that a fit search can be pickled
clf = MockClassifier()
grid_search = GridSearchCV(clf, {'foo_param': [1, 2, 3]}, refit=True)
grid_search.fit(X, y)
grid_search_pickled = pickle.loads(pickle.dumps(grid_search))
assert_array_almost_equal(grid_search.predict(X),
grid_search_pickled.predict(X))
random_search = RandomizedSearchCV(clf, {'foo_param': [1, 2, 3]},
refit=True, n_iter=3)
random_search.fit(X, y)
random_search_pickled = pickle.loads(pickle.dumps(random_search))
assert_array_almost_equal(random_search.predict(X),
random_search_pickled.predict(X))
def test__extract_arfftrace(self):
param_grid = {"max_depth": [3, None],
"max_features": [1, 2, 3, 4],
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
num_iters = 10
task = openml.tasks.get_task(20)
clf = RandomizedSearchCV(RandomForestClassifier(), param_grid, num_iters)
# just run the task
train, _ = task.get_train_test_split_indices(0, 0)
X, y = task.get_X_and_y()
clf.fit(X[train], y[train])
trace_attribute_list = _extract_arfftrace_attributes(clf)
trace_list = _extract_arfftrace(clf, 0, 0)
self.assertIsInstance(trace_attribute_list, list)
self.assertEquals(len(trace_attribute_list), 5 + len(param_grid))
self.assertIsInstance(trace_list, list)
self.assertEquals(len(trace_list), num_iters)
# found parameters
optimized_params = set()
for att_idx in range(len(trace_attribute_list)):
att_type = trace_attribute_list[att_idx][1]
att_name = trace_attribute_list[att_idx][0]
if att_name.startswith("parameter_"):
# add this to the found parameters
param_name = att_name[len("parameter_"):]
optimized_params.add(param_name)
for line_idx in range(len(trace_list)):
val = json.loads(trace_list[line_idx][att_idx])
legal_values = param_grid[param_name]
self.assertIn(val, legal_values)
else:
# repeat, fold, itt, bool
for line_idx in range(len(trace_list)):
val = trace_list[line_idx][att_idx]
if isinstance(att_type, list):
self.assertIn(val, att_type)
elif att_name in ['repeat', 'fold', 'iteration']:
self.assertIsInstance(trace_list[line_idx][att_idx], int)
else: # att_type = real
self.assertIsInstance(trace_list[line_idx][att_idx], float)
self.assertEqual(set(param_grid.keys()), optimized_params)
def test_large_grid():
"""In this test, we purposely overfit a RandomForest to completely random data
in order to assert that the test error will far supercede the train error.
"""
if not SK18:
custom_cv = KFold(n=y_train.shape[0], n_folds=3, shuffle=True, random_state=42)
else:
custom_cv = KFold(n_splits=3, shuffle=True, random_state=42)
# define the pipe
pipe = Pipeline([
('scaler', SelectiveScaler()),
('pca', SelectivePCA(weight=True)),
('rf', RandomForestClassifier(random_state=42))
])
# define hyper parameters
hp = {
'scaler__scaler': [StandardScaler(), RobustScaler(), MinMaxScaler()],
'pca__whiten': [True, False],
'pca__weight': [True, False],
'pca__n_components': uniform(0.75, 0.15),
'rf__n_estimators': randint(5, 10),
'rf__max_depth': randint(5, 15)
}
# define the grid
grid = RandomizedSearchCV(pipe, hp, n_iter=2, scoring='accuracy', n_jobs=1, cv=custom_cv, random_state=42)
# this will fail because we haven't fit yet
assert_fails(grid.score, (ValueError, AttributeError), X_train, y_train)
# fit the grid
grid.fit(X_train, y_train)
# score for coverage -- this might warn...
with warnings.catch_warnings():
warnings.simplefilter("ignore")
grid.score(X_train, y_train)
# coverage:
assert grid._estimator_type == 'classifier'
# get predictions
tr_pred, te_pred = grid.predict(X_train), grid.predict(X_test)
# evaluate score (SHOULD be better than random...)
accuracy_score(y_train, tr_pred), accuracy_score(y_test, te_pred)
# grid score reports:
# assert fails for bad percentile
assert_fails(report_grid_score_detail, ValueError, **{'random_search': grid, 'percentile': 0.0})
assert_fails(report_grid_score_detail, ValueError, **{'random_search': grid, 'percentile': 1.0})
# assert fails for bad y_axis
assert_fails(report_grid_score_detail, ValueError, **{'random_search': grid, 'y_axis': 'bad_axis'})
# assert passes otherwise
report_grid_score_detail(grid, charts=True, percentile=0.95) # just ensure percentile works
def fit(x, y, estimator, dataframe, params):
vectorizer = CountVectorizer(stop_words=['go', '', ' '], binary=False, lowercase=True)
vectorizer.fit(dataframe[x].values)
fresh_estimator = clone(estimator)
x_np, y_np, feature_names, selector = \
select_features(
df = dataframe,
vectorizer=vectorizer,
feature_col=x,
label_col=y,
select_method=None,
continuous_col=None
)
estimator = RandomizedSearchCV(estimator, params, n_iter=60, cv=3, n_jobs=3, refit=True)
estimator.fit(x_np, y_np)
best_params = estimator.best_params_
if method not in ['lr', 'svm']:
print("Calibrating...")
estimator = CalibratedClassifierCV(fresh_estimator.set_params(**best_params), 'isotonic', 3)
estimator.fit(x_np, y_np)
from sklearn.base import _pprint
_pprint(estimator.get_params(deep=True), offset=2)
return estimator, selector, vectorizer
def model_param_search(estimator, X, y, param_dist, scoring,
n_iter=1, n_cv=5, verbose=10, random_state=1, model_id='model', save_search=True):
start = time.time()
random_search = RandomizedSearchCV(estimator, param_distributions=param_dist,
n_iter=n_iter, scoring=scoring, cv=n_cv,
verbose=verbose, random_state=random_state)
random_search.fit(X, y)
print('Best param: ', random_search.best_params_)
print('Best score: ', random_search.best_score_)
print('Best model: ', random_search.best_estimator_)
if save_search:
with open(model_id+'.pickle', 'wb') as f:
pickle.dump(random_search, f)
print('Time searching param for {}: {}'.format(
model_id, (time.time() - start) / 60))
return random_search.best_estimator_
def Stacking(real_train_tar):
predictions_train = pd.DataFrame([np.expm1(y_lasso_predict), np.expm1(y_ridge_predict), np.expm1(y_rf_predict), np.expm1(y_xgb_predict)]).T
sns.pairplot(predictions_train)
learning_rate = [round(float(x), 2) for x in np.linspace(start = .1, stop = .2, num = 11)]
# Minimum for sum of weights for observations in a node
min_child_weight = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
# Maximum nodes in each tree
max_depth = [int(x) for x in np.linspace(1, 10, num = 10)]
n_estimators=[int(x) for x in np.linspace(start = 100, stop = 2000, num = 20)]
subsample=[0.3, 0.4,0.5,0.6, 0.7]
stack_model = xgb.XGBRegressor()
random_grid = {'learning_rate': learning_rate,
'max_depth': max_depth,
'min_child_weight': min_child_weight,
'subsample': subsample,
'n_estimators':n_estimators
}
# Make a RandomizedSearchCV object with correct model and specified hyperparams
xgb_stack = RandomizedSearchCV(estimator=stack_model, param_distributions=random_grid, n_iter=1000, cv=5, verbose=2, random_state=42, n_jobs=-1)
start = time.time()
# Fit models
xgb_stack.fit(predictions_train, real_train_tar)
xgb_stack.best_params_
write_pkl(xgb_stack.best_params_, '/Users/vickywinter/Documents/NYC/Machine Learning Proj/Pickle/stack_params.pkl')
model_stacking = XGBRegressor(**xgb_stack.best_params_)
#model_xgb = XGBRegressor(**best_params_)
start=time.time()
model_stacking.fit(predictions_train,real_train_tar)
end=time.time()
print("MSE for train data is: %f" % mean_squared_error(np.log1p(real_train_tar),np.log1p( model_stacking.predict(predictions_train))))
print('Time elapsed: %.4f seconds' % (end-start))
y_stack_predict=model_stacking.predict(predictions_train)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,y_stack_predict)
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
def test_grid_search_with_multioutput_data():
# Test search with multi-output estimator
X, y = make_multilabel_classification(return_indicator=True,
random_state=0)
est_parameters = {"max_depth": [1, 2, 3, 4]}
cv = KFold(random_state=0)
estimators = [DecisionTreeRegressor(random_state=0),
DecisionTreeClassifier(random_state=0)]
# Test with grid search cv
for est in estimators:
grid_search = GridSearchCV(est, est_parameters, cv=cv)
grid_search.fit(X, y)
res_params = grid_search.cv_results_['params']
for cand_i in range(len(res_params)):
est.set_params(**res_params[cand_i])
for i, (train, test) in enumerate(cv.split(X, y)):
est.fit(X[train], y[train])
correct_score = est.score(X[test], y[test])
assert_almost_equal(
correct_score,
grid_search.cv_results_['split%d_test_score' % i][cand_i])
# Test with a randomized search
for est in estimators:
random_search = RandomizedSearchCV(est, est_parameters,
cv=cv, n_iter=3)
random_search.fit(X, y)
res_params = random_search.cv_results_['params']
for cand_i in range(len(res_params)):
est.set_params(**res_params[cand_i])
for i, (train, test) in enumerate(cv.split(X, y)):
est.fit(X[train], y[train])
correct_score = est.score(X[test], y[test])
assert_almost_equal(
correct_score,
random_search.cv_results_['split%d_test_score'
% i][cand_i])
def train_classifier(self, trainvectors, labels, c='1.0', kernel='linear', gamma='0.1', degree='1', class_weight='balanced', jobs=1, iterations=10, scoring='f1_micro', v=2):
if len(list(set(labels))) > 2: # more than two classes to distinguish
parameters = ['estimator__C', 'estimator__kernel', 'estimator__gamma', 'estimator__degree']
multi = True
else: # only two classes to distinguish
parameters = ['C', 'kernel', 'gamma', 'degree']
multi = False
if len(class_weight.split(':')) > 1: # dictionary
class_weight = dict([label_weight.split(':') for label_weight in class_weight.split()])
c_values = [0.001, 0.005, 0.01, 0.5, 1, 5, 10, 50, 100, 500, 1000] if c == 'search' else [float(x) for x in c.split()]
kernel_values = ['linear', 'rbf', 'poly'] if kernel == 'search' else [k for k in kernel.split()]
gamma_values = [0.0005, 0.002, 0.008, 0.032, 0.128, 0.512, 1.024, 2.048] if gamma == 'search' else [float(x) for x in gamma.split()]
degree_values = [1, 2, 3, 4] if degree == 'search' else [int(x) for x in degree.split()]
grid_values = [c_values, kernel_values, gamma_values, degree_values]
if not False in [len(x) == 1 for x in grid_values]: # only sinle parameter settings
settings = {}
for i, parameter in enumerate(parameters):
settings[parameter] = grid_values[i][0]
else:
param_grid = {}
for i, parameter in enumerate(parameters):
param_grid[parameter] = grid_values[i]
model = svm.SVC(probability=True)
if multi:
model = OutputCodeClassifier(model)
trainvectors = trainvectors.todense()
paramsearch = RandomizedSearchCV(model, param_grid, cv = 5, scoring=scoring, verbose = v, n_iter = iterations, n_jobs = jobs, pre_dispatch = 4)
paramsearch.fit(trainvectors, labels)
settings = paramsearch.best_params_
# train an SVC classifier with the settings that led to the best performance
self.model = svm.SVC(
probability = True,
C = settings[parameters[0]],
kernel = settings[parameters[1]],
gamma = settings[parameters[2]],
degree = settings[parameters[3]],
class_weight = class_weight,
cache_size = 1000,
verbose = v
)
self.model.fit(trainvectors, labels)
def build_lasso(x_train, y_train, x_test, y_test, n_features):
"""
Constructing a Lasso linear model with cross validation from input dataframe
:param x_train: features dataframe for model training
:param y_train: target dataframe for model training
:param x_test: features dataframe for model testing
:param y_test: target dataframe for model testing
:return: None
"""
model = Lasso(random_state=1)
# Random state has int value for non-random sampling
param_dist = {'alpha': np.arange( 0.0001, 1, 0.001 ).tolist()}
clf = RandomizedSearchCV(estimator=model, param_distributions=param_dist,
n_iter=15, n_jobs=-1)
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
print(clf.best_params_, clf.best_score_)
# Mean absolute error regression loss
mean_abs = sklearn.metrics.mean_absolute_error(y_test, y_pred)
# Mean squared error regression loss
mean_sq = sklearn.metrics.mean_squared_error(y_test, y_pred)
# Median absolute error regression loss
median_abs = sklearn.metrics.median_absolute_error(y_test, y_pred)
# R^2 (coefficient of determination) regression score function
r2 = sklearn.metrics.r2_score(y_test, y_pred)
# Explained variance regression score function
exp_var_score = sklearn.metrics.explained_variance_score(y_test, y_pred)
with open('../trained_networks/lasso_%d_data.pkl' % n_features, 'wb') as results:
pickle.dump(clf, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(mean_abs, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(mean_sq, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(median_abs, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(r2, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(exp_var_score, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(y_pred, results, pickle.HIGHEST_PROTOCOL)
return
def test_randomized_search_grid_scores():
# Make a dataset with a lot of noise to get various kind of prediction
# errors across CV folds and parameter settings
X, y = make_classification(n_samples=200, n_features=100, n_informative=3,
random_state=0)
# XXX: as of today (scipy 0.12) it's not possible to set the random seed
# of scipy.stats distributions: the assertions in this test should thus
# not depend on the randomization
params = dict(C=expon(scale=10),
gamma=expon(scale=0.1))
n_cv_iter = 3
n_search_iter = 30
search = RandomizedSearchCV(SVC(), n_iter=n_search_iter, cv=n_cv_iter,
param_distributions=params, iid=False)
search.fit(X, y)
assert_equal(len(search.grid_scores_), n_search_iter)
# Check consistency of the structure of each cv_score item
for cv_score in search.grid_scores_:
assert_equal(len(cv_score.cv_validation_scores), n_cv_iter)
# Because we set iid to False, the mean_validation score is the
# mean of the fold mean scores instead of the aggregate sample-wise
# mean score
assert_almost_equal(np.mean(cv_score.cv_validation_scores),
cv_score.mean_validation_score)
assert_equal(list(sorted(cv_score.parameters.keys())),
list(sorted(params.keys())))
# Check the consistency with the best_score_ and best_params_ attributes
sorted_grid_scores = list(sorted(search.grid_scores_,
key=lambda x: x.mean_validation_score))
best_score = sorted_grid_scores[-1].mean_validation_score
assert_equal(search.best_score_, best_score)
tied_best_params = [s.parameters for s in sorted_grid_scores
if s.mean_validation_score == best_score]
assert_true(search.best_params_ in tied_best_params,
"best_params_={0} is not part of the"
" tied best models: {1}".format(
search.best_params_, tied_best_params))
def build_tree(x_train, y_train, x_test, y_test, n_features):
"""
Constructing a decision trees regression model from input dataframe
:param x_train: features dataframe for model training
:param y_train: target dataframe for model training
:param x_test: features dataframe for model testing
:param y_test: target dataframe for model testing
:return: None
"""
model = DecisionTreeRegressor()
param_dist = {'max_depth': sp_randint(1, 15),
'min_samples_split': sp_randint(2, 15)}
clf = RandomizedSearchCV(estimator=model, param_distributions=param_dist,
n_iter=15, n_jobs=-1)
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
print(clf.best_params_, clf.best_score_)
# Mean absolute error regression loss
mean_abs = sklearn.metrics.mean_absolute_error(y_test, y_pred)
# Mean squared error regression loss
mean_sq = sklearn.metrics.mean_squared_error(y_test, y_pred)
# Median absolute error regression loss
median_abs = sklearn.metrics.median_absolute_error(y_test, y_pred)
# R^2 (coefficient of determination) regression score function
r2 = sklearn.metrics.r2_score(y_test, y_pred)
# Explained variance regression score function
exp_var_score = sklearn.metrics.explained_variance_score(y_test, y_pred)
with open('../trained_networks/dt_%d_data.pkl' % n_features, 'wb') as results:
pickle.dump(clf, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(mean_abs, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(mean_sq, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(median_abs, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(r2, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(exp_var_score, results, pickle.HIGHEST_PROTOCOL)
pickle.dump(y_pred, results, pickle.HIGHEST_PROTOCOL)
return
def random_grid_search_tuning(self,cat_param, cat_param_distribution, f_score, n_jobs, n_iter):
cat_estimator = cat.CatBoostClassifier(**cat_param)
cat_rgs = RandomizedSearchCV(
estimator=cat_estimator,
param_distributions=cat_param_distribution,
cv=self.skf,
scoring=make_scorer(f_score, greater_is_better=True, needs_proba=True),
n_iter=n_iter,
n_jobs=n_jobs,
verbose=2,
refit=False,
)
time_begin = time.time()
cat_rgs.fit(self.X, self.y)
time_end = time.time()
logging.info('Random grid search eat time {0}'.format(time_end - time_begin))
logging.info('best_score_ : {0}'.format(cat_rgs.best_score_))
logging.info('best_params_ : {0}'.format(cat_rgs.best_params_))
for score in cat_rgs.grid_scores_:
logging.info('grid_scores_ : {0}'.format(score))
gc.collect()
return cat_rgs.best_params_
def evaluate_model(self, pipelines):
n,m = pipelines
parameters = self.get_params(n, self.optimizer)
if self.optimizer == 'GridSearchCV':
print("Performing GridSearchCV...", n)
grid_search_t = GridSearchCV(m, parameters, verbose=1)
grid_search_t.fit(self.evaluator.X_train, self.evaluator.y_train)
return [grid_search_t.best_score_,grid_search_t.best_params_]
elif self.optimizer == 'RandomizedSearchCV':
print("Performing RandomizedSearchCV...", n)
random_search_t = RandomizedSearchCV(m, parameters, verbose=1)
random_search_t.fit(self.evaluator.X_train, self.evaluator.y_train)
return [random_search_t.best_score_,random_search_t.best_params_]
elif self.optimizer == 'GeneticSearchCV':
print("Performing GeneticSearchCV...", n)
genetic_search_t = GeneticSearchCV(m, parameters, scoring=None, cv=KFold(n_splits=5), n_jobs=1, verbose=1, refit=False, population_size=50, gene_mutation_prob=0.10, gene_crossover_prob=0.5, tournament_size=3, generations_number=10)
genetic_search_t.fit(self.evaluator.X_train, self.evaluator.y_train)
return [genetic_search_t.best_score_,genetic_search_t.best_params_]
elif self.optimizer == 'EdasSearch':
print("Performing EdasSearch...", n)
eda_search_t = EdasSearch(getModelAccuracy, parameters, m,iterations=2, sample_size=15, select_ratio=0.3, debug=False, n_jobs=1)
eda_search_t.fit()
return [eda_search_t.best_score_,eda_search_t.best_params_]
def train_classifier(self, trainvectors, labels, n_neighbors='3', weights='uniform', algorithm='auto', leaf_size='30', metric='euclidean', p=2, scoring='roc_auc', jobs=1, v=2):
if len(list(set(labels))) > 2: # more than two classes to distinguish
parameters = ['estimator__n_neighbors','estimator__weights', 'estimator__leaf_size', 'estimator__metric']
multi = True
else: # only two classes to distinguish
parameters = ['n_neighbors','weights', 'leaf_size', 'metric']
multi = False
n_neighbours = [3,5,7,9] if n_neighbors == 'search' else [int(x) for x in n_neighbors.split()]
weights = ['uniform','distance'] if weights == 'search' else weights.split()
leaf_size = [10,20,30,40,50] if n_neighbors == 'search' else [int(x) for x in leaf_size.split()]
metric = ['minkowski','euclidean','manhattan','hamming'] if metric == 'search' else metric.split()
grid_values = [n_neighbors, weights, leaf_size, metric]
if not False in [len(x) == 1 for x in grid_values]: # only sinle parameter settings
settings = {}
for i, parameter in enumerate(parameters):
settings[parameter] = grid_values[i][0]
else:
param_grid = {}
for i, parameter in enumerate(parameters):
param_grid[parameter] = grid_values[i]
model = KNeighborsClassifier(algorithm=algorithm,p=p)
if multi:
model = OutputCodeClassifier(model)
trainvectors = trainvectors.todense()
paramsearch = RandomizedSearchCV(model, param_grid, verbose=v, scoring=scoring, cv=5, n_jobs=jobs)
paramsearch.fit(trainvectors, labels)
settings = paramsearch.best_params_
self.model = KNeighborsClassifier(
algorithm=algorithm,
p=p,
n_neighbors=settings[parameters[0]],
weights=settings[parameters[1]],
leaf_size=settings[parameters[2]],
metric=settings[parameters[3]]
)
self.model.fit(trainvectors, labels)
def model(clf_name, features, labels):
start_time = time.time()
# specify parameters and distributions to sample from
clf = make_pipeline(StandardScaler(), PCA(), classifiers[clf_name]) #PCA optional: n_components=2
'''clf = Pipeline([
('reduce_dim', PCA()),
('classify', classifiers[clf_name])
])'''
# select correct param set, adjust the pca to current window
param_dist = param_dict[clf_name]
param_dist["pca__n_components"] = sp_randint(2, features.shape[1]-1)
#if 'randomforestclassifier__max_features' in param_dist:
# param_dist['randomforestclassifier__max_features'] = sp_randint(2, features.shape[1])
# run randomized search
n_iter_search = 50
random_search = RandomizedSearchCV(clf, param_distributions=param_dist,
n_iter=n_iter_search,
scoring="f1_weighted")#,
#n_jobs = 8)#,pre_dispatch=8)
#start = time.time()
random_search.fit(features, labels)
#print("RandomizedSearchCV took %.2f seconds for %d candidates"
# " parameter settings." % ((time.time() - start), n_iter_search))
#report(random_search.cv_results_)
elapsed_time = time.time() - start_time
print 'Optimizing %s on window %d took %d sec'%(clf_name, features.shape[1]/6, elapsed_time)
return random_search
def test_random_search_cv_results():
# Make a dataset with a lot of noise to get various kind of prediction
# errors across CV folds and parameter settings
X, y = make_classification(n_samples=200, n_features=100, n_informative=3,
random_state=0)
# scipy.stats dists now supports `seed` but we still support scipy 0.12
# which doesn't support the seed. Hence the assertions in the test for
# random_search alone should not depend on randomization.
n_splits = 3
n_search_iter = 30
params = dict(C=expon(scale=10), gamma=expon(scale=0.1))
random_search = RandomizedSearchCV(SVC(), n_iter=n_search_iter,
cv=n_splits, iid=False,
param_distributions=params)
random_search.fit(X, y)
random_search_iid = RandomizedSearchCV(SVC(), n_iter=n_search_iter,
cv=n_splits, iid=True,
param_distributions=params)
random_search_iid.fit(X, y)
param_keys = ('param_C', 'param_gamma')
score_keys = ('mean_test_score', 'mean_train_score',
'rank_test_score',
'split0_test_score', 'split1_test_score',
'split2_test_score',
'split0_train_score', 'split1_train_score',
'split2_train_score',
'std_test_score', 'std_train_score',
'mean_fit_time', 'std_fit_time',
'mean_score_time', 'std_score_time')
n_cand = n_search_iter
for search, iid in zip((random_search, random_search_iid), (False, True)):
assert_equal(iid, search.iid)
cv_results = search.cv_results_
# Check results structure
check_cv_results_array_types(cv_results, param_keys, score_keys)
check_cv_results_keys(cv_results, param_keys, score_keys, n_cand)
# For random_search, all the param array vals should be unmasked
assert_false(any(cv_results['param_C'].mask) or
any(cv_results['param_gamma'].mask))
check_cv_results_grid_scores_consistency(search)
n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]
max_depth.append(None)
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
params = {'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'bootstrap': bootstrap}
cv_result = RandomizedSearchCV(RandomForestClassifier(),params,cv=3,scoring='accuracy',random_state = 5)
cv_result.fit(X_resampled, y_resampled)
cv_result.best_params_
# In[114]:
classifier_op = RandomForestClassifier(n_estimators = 2000, min_samples_split=2,min_samples_leaf=1,max_features='auto',
max_depth=70, random_state = 42,bootstrap=False)
classifier_op.fit(X_resampled, y_resampled)
# In[115]:
return model
def create_hyperparameters():
batches = [10, 20]
optimizers = ['rmsprop', 'adam', 'adadelta']
dropout = [0.1]
return {'batch_size': batches, 'optimizer': optimizers, 'drop': dropout}
hyperparameters = create_hyperparameters()
# 우리가 만든 케라스 모델을 싸이킷런에 넣을수 있게 wrapping!
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
model = KerasClassifier(build_fn=build_model, verbose=0)
# wrapping된 모델을 이용해서 싸이킷런의 GridSearchCV 이용!
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
# search = GridSearchCV(model, hyperparameters, cv=3)
search = RandomizedSearchCV(model, hyperparameters, cv=3)
search.fit(x_train, y_train)
acc = search.score(x_test, y_test)
print('최적의 파라미터 :', search.best_params_)
print('최종 스코어:', acc)
print('hyper_cnn')
'''
최적의 파라미터 : {'optimizer': 'adam', 'drop': 0.1, 'batch_size': 20}
최종 스코어: 0.9825000166893005
'''
batches = [50,100,120]
optimizers = ['rmsprop', 'adam', 'adadelta'] # 용도에 맞게 쓰자.
dropout = np.linspace(0.1, 0.25, 0.5, 5)
epochs = [1,2]
return{"kerasclassifier__batch_size":batches, "kerasclassifier__optimizer":optimizers, "kerasclassifier__epochs":epochs} #, "keep_prob":dropout}
# 밑에서 make를 쓸때는 각 parameter앞에 kerasclassifier__를, 그냥 Pipeline 쓸때는 svc__를 써주면 됨!
from keras.wrappers.scikit_learn import KerasClassifier # 사이킷런과 호환하도록 함. (mnist에서 쓸듯)
# from keras.wrappers.scikit_learn import KerasRegressor # 사이킷런의 교차검증을 keras에서 사용하기 위해 wrapping함
model = KerasClassifier(build_fn=build_network, verbose=1) # verbose=0 위에서 만든 함수형 모델 당겨옴.
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import make_pipeline
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
hyperparameters = create_hyperparameters()
pipe = make_pipeline(MinMaxScaler(), model)
from sklearn.model_selection import RandomizedSearchCV
search = RandomizedSearchCV(estimator=pipe,
param_distributions=hyperparameters,
n_iter=10, n_jobs=1, cv=3, verbose=1)
# # 작업이 10회 수행, 3겹 교차검증 사용(3조각을 나눠서 검증). n_jobs는 알아서 찾아볼 것.
# KFold가 5번 돌았다면 얘는 랜덤하게 돈다. 이 작업을 하는 것은 위의 하이퍼파라미터 중 최적의 결과를 내는 파라미터들을 찾기 위함.
# search.fit(data["x_train"], data["y_train"])
search.fit(x_train, y_train) # 데이터 집어넣기!
print(search.best_score_)
print(search.best_params_)
results['std_test_score'][candidate]))
print("Parameters: {0}".format(results['params'][candidate]))
print("")
# specify parameters and distributions to sample from
param_dist = {"max_depth": [3, None],
"max_features": sp_randint(1, 6),
"min_samples_split": sp_randint(2, 11),
"min_samples_leaf": sp_randint(2, 11),
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
# run randomized search
n_iter_search = 20
random_search = RandomizedSearchCV(clf, param_distributions=param_dist,
n_iter=n_iter_search)
start = time()
random_search.fit(info_train, OL_train)
print("RandomizedSearchCV took %.2f seconds for %d candidates"
" parameter settings." % ((time() - start), n_iter_search))
report(random_search.cv_results_, n_top=10)
stop
forest = RandomForestClassifier(n_estimators=100, **random_search.best_params_)
scores = cross_val_score(forest, class_info, stars)
print scores.mean()
forest.fit(info_train, OL_train)
#joblib.dump(forest, 'trained_forest_classifier.pkl')
X = train_df.drop("Survived", axis=1).copy()
y = train_df["Survived"]
# In[ ]:
param_grid = {
'max_depth': st.randint(6, 11),
'n_estimators': st.randint(300, 500),
'max_features': np.arange(0.5, .81, 0.05),
'max_leaf_nodes': st.randint(6, 10)
}
grid = RandomizedSearchCV(rfc,
param_grid,
cv=10,
scoring='accuracy',
verbose=1,
n_iter=20)
grid.fit(X, y)
# In[ ]:
grid.best_estimator_
# In[ ]:
grid.best_score_
# Ok so now let's generate our predictions based on the best estimator model.
# specify parameters for hyperparameter search
# specify parameters and distributions to sample from
param_dist = {
"max_depth": [6, None],
"max_features": sp_randint(1, max_features),
"min_samples_split": sp_randint(2, 30),
"min_samples_leaf": sp_randint(1, 30),
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]
}
# run randomized search
n_iter_search = _RF_iterations
random_search = RandomizedSearchCV(
clf,
param_distributions=param_dist,
n_iter=n_iter_search,
verbose=_VERBOSITY
) # http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.RandomizedSearchCV.html
# let's start training the model.
print("Beginning hyper-parameter search")
start = time()
random_search.fit(X_train, y_train)
print("RandomizedSearchCV took %.2f seconds for %d candidates"
" parameter settings." % ((time() - start), n_iter_search))
print(random_search.best_params_)
# now let's train a model on the entire training set
# with those parameters
results['std_test_score'][candidate]))
print("Parameters: {0}".format(results['params'][candidate]))
print("")
# specify parameters and distributions to sample from
param_dist = {"max_depth": [3, None],
"max_features": sp_randint(1, 11),
"min_samples_split": sp_randint(2, 11),
"min_samples_leaf": sp_randint(1, 11),
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
# run randomized search
n_iter_search = 20
random_search = RandomizedSearchCV(clf, param_distributions=param_dist,
n_iter=n_iter_search)
start = time()
random_search.fit(X, y)
print("RandomizedSearchCV took %.2f seconds for %d candidates"
" parameter settings." % ((time() - start), n_iter_search))
report(random_search.cv_results_)
# use a full grid over all parameters
param_grid = {"max_depth": [3, None],
"max_features": [1, 3, 10],
"min_samples_split": [2, 3, 10],
"min_samples_leaf": [1, 3, 10],
"bootstrap": [True, False],
"criterion": ["gini", "entropy"]}
from scipy.stats import uniform as sp_uniform
# Create parameters grid for RBF kernel, we have to set C and gamma
C_dist = sp_uniform(scale=10)
gamma_dist = sp_uniform(scale=1)
parameters = {'kernel':['rbf'],
'C':C_dist,
'gamma': gamma_dist
}
from sklearn.model_selection import RandomizedSearchCV
n_iter_search = 8
svm_clsf = svm.SVC()
rnd_clsf = RandomizedSearchCV(estimator=svm_clsf,
param_distributions=parameters,
n_iter=n_iter_search,
cv=3,
n_jobs=1,
verbose=2)
# Warning! It takes really long time to compute this about 2 days
start_time = dt.datetime.now()
print('Start param searching at {}'.format(str(start_time)))
rnd_clsf.fit(X_train, y_train)
elapsed_time= dt.datetime.now() - start_time
print('Elapsed time, param searching {}'.format(str(elapsed_time)))
sorted(rnd_clsf.cv_results_.keys())
classifier = rnd_clsf.best_estimator_
params = rnd_clsf.best_params_
'bootstrap': bootstrap,
'max_samples': max_samples
}
pprint(random_grid)
#%%
# Use the random grid to search for best hyperparameters
# First create the base model to tune
rf = RandomForestRegressor()
# Random search of parameters, using 3 fold cross validation,
# search across 10000 different combinations, and use all available cores
rf_random = RandomizedSearchCV(estimator=rf,
param_distributions=random_grid,
scoring='neg_mean_absolute_error',
n_iter=10000,
cv=5,
verbose=2,
random_state=42,
n_jobs=-1)
# Fit the random search model
rf_random.fit(train_features, train_labels)
#%%
# Create dataframe with metrics of every single combination tested
random_results = pd.DataFrame(rf_random.cv_results_)
random_results = random_results.sort_values('rank_test_score')
random_results.to_json(
r'/Users/matthew/Desktop/data/RF_randomized_search_CV_mae.json')
# set RF with best parameters from random sampling
print(rf_random.best_params_)
def run_test(filename, results_dir, models, random_state, external_split,
internal_split, optimization_iterations):
global df_results
print(filename)
data_dict['Dataset Name'] = filename.replace('.csv', '')
df = pd.read_csv(directory + '/' + filename)
X, Y = fix_dataset(df)
kf = StratifiedKFold(n_splits=external_split,
random_state=random_state,
shuffle=True)
for fold_index, (train_index, test_index) in enumerate(kf.split(X, Y)):
data_dict['Cross Validation[1-10]'] = fold_index
print("fold index =", fold_index)
x_train = X.iloc[train_index]
y_train = Y.iloc[train_index]
x_test = X.iloc[test_index]
y_test = Y.iloc[test_index]
for model_name, model_class, model, model_dict in models:
print('Model:', model_name)
data_dict['Algorithm Name'] = model_name
# distributions = dict(C=uniform(loc=0, scale=4), penalty=['l2', 'l1'])
distributions = model_dict
start_training_time = time.time()
randomSearcher = RandomizedSearchCV(
model,
distributions,
random_state=random_state,
cv=internal_split,
n_iter=optimization_iterations,
scoring=make_scorer(accuracy_score))
randomSearcher.fit(x_train, y_train.values.ravel())
if model_class is wprb:
params = {
k.replace("estimator__", ""): v
for k, v in randomSearcher.best_params_.items()
}
best_model = OneVsRestClassifier(model_class(**params))
else:
params = randomSearcher.best_params_
best_model = model_class(**params)
data_dict['Hyper-Parameters Values'] = params
best_model.fit(x_train, y_train.values.ravel())
data_dict['Training Time'] = time.time() - start_training_time
print("best params:", params)
print(
"train accuracy:",
round(accuracy_score(y_train, best_model.predict(x_train)), 4))
start_inference_time = time.time()
test_pred = best_model.predict(x_test)
test_pred_proba = best_model.predict_proba(x_test)
data_dict['Inference Time'] = (
time.time() - start_inference_time) / (len(x_test)) * 1000
print("test accuracy:", round(accuracy_score(y_test, test_pred),
4))
print()
data_dict['Accuracy'] = accuracy_score(y_test, test_pred)
data_dict['Precision'] = precision_score(
y_test,
test_pred,
average='macro',
labels=np.unique(test_pred))
unique_labels = np.unique(Y.values)
if len(unique_labels) == 2: # multiclass vs binary classification
data_dict['AUC'] = roc_auc_score(y_true=y_test,
y_score=test_pred_proba[:, 1])
else:
# plaster = test_pred_proba[:, [np.where(np.unique(Y.values) == x)[0][0] for x in np.unique(y_test)]]
# plaster2 = np.array([[x / sum(y) for x in y] for y in plaster])
data_dict['AUC'] = roc_auc_score(y_true=y_test,
y_score=test_pred_proba,
multi_class='ovr',
labels=np.unique(y_test))
all_TPR = []
all_FPR = []
all_PR_CURVE = []
for index, class_label in enumerate(np.unique(y_test)):
tn, fp, fn, tp = confusion_matrix(
y_test == class_label, test_pred == class_label).ravel()
all_FPR.append(fp / (fp + tn))
all_TPR.append(tp / (tp + fn))
precision, recall, _ = precision_recall_curve(
y_test == class_label, test_pred_proba[:, index])
all_PR_CURVE.append(auc(recall, precision))
data_dict['FPR'] = np.mean(all_FPR)
data_dict['TPR'] = np.mean(all_TPR)
data_dict['PR Curve'] = np.mean(all_PR_CURVE)
df_results = df_results.append(data_dict, ignore_index=True)
df_results.to_csv(results_dir + '/' + filename, index=False)
df_results = df_results.iloc[0:0]
def Model(train_linear, test_linear):
train_linear_fea=train_linear.drop(columns=['SalePrice'])
train_linear_tar=train_linear.SalePrice
x_train, x_test, y_train, y_test = train_test_split(train_linear_fea, train_linear_tar,test_size=0.2, random_state=0)
def evaluate(model, test_features, test_labels,train_features, train_labels):
predictions = model.predict(test_features)
errors = abs(predictions - test_labels)
mape = 100 * np.mean(errors / test_labels)
accuracy = 100 - mape
print('Model Performance')
print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))
print('Accuracy = {:0.2f}%.'.format(accuracy))
print("MSE for train data is: %f" % mean_squared_error(y_train, model.predict(x_train)))
print("MSE for validation data is: %f" % mean_squared_error(y_test, model.predict(x_test)))
return accuracy
real_train_tar=np.expm1(train_linear_tar)
"""
. Lasso model
"""
lassocv = LassoCV(alphas = np.logspace(-5, 4, 400), )
lassocv.fit(train_linear_fea, train_linear_tar)
lassocv_score = lassocv.score(train_linear_fea, train_linear_tar)
lassocv_alpha = lassocv.alpha_
print("Best alpha : ", lassocv_alpha, "Score: ",lassocv_score)
start=time.time()
lasso =Lasso(normalize = True)
lasso.set_params(alpha=lassocv_alpha,max_iter = 10000)
lasso.fit(x_train, y_train)
end=time.time()
mean_squared_error(y_test, lasso.predict(x_test))
coef_lasso=pd.Series(lassocv.coef_, index=x_train.columns).sort_values(ascending =False)
evaluate(lasso,x_test,y_test,x_train,y_train)
print('Time elapsed: %.4f seconds' % (end-start))
y_lasso_predict=lasso.predict(train_linear_fea)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,np.expm1(y_lasso_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
test_prediction_lasso=np.expm1(lasso.predict(test_linear))
"""
. Ridge model
"""
ridgecv = RidgeCV(alphas = np.logspace(-5, 4, 400))
ridgecv.fit(x_train, y_train)
ridgecv_score = ridgecv.score(x_train, y_train)
ridgecv_alpha = ridgecv.alpha_
print("Best alpha : ", ridgecv_alpha, "Score: ",ridgecv_score)
coef=pd.Series(ridgecv.coef_, index=x_train.columns).sort_values(ascending =False)
start=time.time()
ridge =Ridge(normalize = True)
ridge.set_params(alpha=ridgecv_alpha,max_iter = 10000)
ridge.fit(x_train, y_train)
end=time.time()
mean_squared_error(y_test, ridge.predict(x_test))
coef_ridge=pd.Series(ridgecv.coef_, index=x_train.columns).sort_values(ascending =False)
evaluate(ridge,x_test,y_test,x_train,y_train)
print('Time elapsed: %.4f seconds' % (end-start))
y_ridge_predict=ridge.predict(train_linear_fea)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,np.expm1(y_ridge_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
test_prediction_ridge=np.expm1(ridge.predict(test_linear))
"""
. Random Forest
"""
#train=train.drop(columns=['DateSold'])
#test=test.drop(columns=['DateSold'])
#X_train=train.drop(columns=['SalePrice'])
#Y_train=train['SalePrice']
X_train=train_linear_fea
Y_train=train_linear_tar
x_train_rf, x_test_rf, y_train_rf, y_test_rf = train_test_split(X_train, Y_train,test_size=0.2, random_state=0)
n_estimators = [int(x) for x in np.linspace(start = 100, stop = 2000, num = 20)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
random_grid = {'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'bootstrap': bootstrap}
rf = RandomForestRegressor()
# Random search of parameters, using 3 fold cross validation,
# search across 100 different combinations, and use all available cores
#
rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)
rf_random.fit(X_train, Y_train)
#rf_random.fit(x_train_rf, y_train_rf)
rf_random.best_params_
#Random search allowed us to narrow down the range for each hyperparameter. Now that we know where to concentrate our search,
# we can explicitly specify every combination of settings to try.
param_grid = {
'bootstrap': [False],
'max_depth': [80, 90, 100, 110,120,130],
'max_features': [2, 3],
'min_samples_leaf': [1,2,3, 4],
'min_samples_split': [2,4,6,8, 10, 12],
'n_estimators': [600,700, 800, 900, 1000]
}
# Create a based model
rf = RandomForestRegressor()
# Instantiate the grid search model
grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, cv = 3, n_jobs = -1, verbose = 2)
#grid_search.fit(x_train, y_train)
grid_search.fit(X_train, Y_train)
grid_search.best_params_
best_random = grid_search.best_estimator_
start=time.time()
best_random.fit(x_train_rf,y_train_rf)
end=time.time()
evaluate(best_random, x_test_rf, y_test_rf,x_train_rf,y_train_rf)
print('Time elapsed: %.4f seconds' % (end-start))
y_rf_predict=best_random.predict(train_linear_fea)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,np.expm1(y_rf_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
importance_rf = pd.DataFrame({'features':train_linear_fea.columns, 'imp':best_random.feature_importances_}).\
sort_values('imp',ascending=False)
importance_top20_rf = importance_rf.iloc[:20,]
plt.barh(importance_top20_rf.features, importance_top20_rf.imp)
plt.xlabel('Feature Importance')
test_prediction_rf=np.expm1(best_random.predict(test_linear))
"""
. Xgboost
"""
learning_rate = [round(float(x), 2) for x in np.linspace(start = .1, stop = .2, num = 11)]
# Minimum for sum of weights for observations in a node
min_child_weight = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
# Maximum nodes in each tree
max_depth = [int(x) for x in np.linspace(1, 10, num = 10)]
n_estimators=[int(x) for x in np.linspace(start = 100, stop = 2000, num = 20)]
subsample=[0.3, 0.4,0.5,0.6, 0.7]
model = xgb.XGBRegressor()
random_grid = {'learning_rate': learning_rate,
'max_depth': max_depth,
'min_child_weight': min_child_weight,
'subsample': subsample,
'n_estimators':n_estimators
}
# Make a RandomizedSearchCV object with correct model and specified hyperparams
xgb_random = RandomizedSearchCV(estimator=model, param_distributions=random_grid, n_iter=1000, cv=5, verbose=2, random_state=42, n_jobs=-1)
start = time.time()
# Fit models
xgb_random.fit(X_train, Y_train)
xgb_random.best_params_
"""
best_params_={'learning_rate': 0.1,
'max_depth': 2,
'min_child_weight': 4,
'n_estimators': 900,
'subsample': 0.5}
"""
model_xgb = XGBRegressor(**xgb_random.best_params_)
#model_xgb = XGBRegressor(**best_params_)
start=time.time()
model_xgb.fit(x_train_rf,y_train_rf)
end=time.time()
evaluate(model_xgb, x_test_rf, y_test_rf,x_train_rf,y_train_rf)
print('Time elapsed: %.4f seconds' % (end-start))
y_xgb_predict=model_xgb.predict(train_linear_fea)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,np.expm1(y_xgb_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
importance_xgb = pd.DataFrame({'features':train_linear_fea.columns, 'imp':model_xgb.feature_importances_}).\
sort_values('imp',ascending=False)
importance_top20_xgb = importance_xgb.iloc[:20,]
plt.barh(importance_top20_xgb.features, importance_top20_xgb.imp)
plt.xlabel('Feature Importance')
test_prediction_xgb=np.expm1(model_xgb.predict(test_linear))
return(test_prediction_lasso, test_prediction_ridge, test_prediction_rf, test_prediction_xgb,y_lasso_predict, y_ridge_predict, y_rf_predict, y_xgb_predict)
def _compute_thresh(this_data, method='bayesian_optimization',
cv=10, y=None, random_state=None):
"""Compute the rejection threshold for one channel.
Parameters
----------
this_data: array (n_epochs, n_times)
Data for one channel.
method : str
'bayesian_optimization' or 'random_search'
cv : iterator
Iterator for cross-validation.
random_state : int seed, RandomState instance, or None (default)
The seed of the pseudo random number generator to use.
Returns
-------
best_thresh : float
The best threshold.
Notes
-----
For method='random_search', the random_state parameter gives deterministic
results only for scipy versions >= 0.16. This is why we recommend using
autoreject with scipy version 0.16 or greater.
"""
est = _ChannelAutoReject()
all_threshes = np.sort(np.ptp(this_data, axis=1))
if method == 'random_search':
param_dist = dict(thresh=uniform(all_threshes[0],
all_threshes[-1]))
rs = RandomizedSearchCV(est,
param_distributions=param_dist,
n_iter=20, cv=cv,
random_state=random_state)
rs.fit(this_data, y)
best_thresh = rs.best_estimator_.thresh
elif method == 'bayesian_optimization':
cache = dict()
def func(thresh):
idx = np.where(thresh - all_threshes >= 0)[0][-1]
thresh = all_threshes[idx]
if thresh not in cache:
est.set_params(thresh=thresh)
obj = -np.mean(cross_val_score(est, this_data, y=y, cv=cv))
cache.update({thresh: obj})
return cache[thresh]
n_epochs = all_threshes.shape[0]
idx = np.concatenate((
np.linspace(0, n_epochs, 40, endpoint=False, dtype=int),
[n_epochs - 1])) # ensure last point is in init
idx = np.unique(idx) # linspace may be non-unique if n_epochs < 40
initial_x = all_threshes[idx]
best_thresh, _ = bayes_opt(func, initial_x,
all_threshes,
expected_improvement,
max_iter=10, debug=False,
random_state=random_state)
return best_thresh
# Import necessary modules
from scipy.stats import randint
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import RandomizedSearchCV
# Setup the parameters and distributions to sample from: param_dist
param_dist = {"max_depth": [3, None],
"max_features": randint(1, 9),
"min_samples_leaf": randint(1, 9),
"criterion": ["gini", "entropy"]}
# Instantiate a Decision Tree classifier: tree
tree = DecisionTreeClassifier()
# Instantiate the RandomizedSearchCV object: tree_cv
tree_cv = RandomizedSearchCV(tree, param_dist, cv=5)
# Fit it to the data
tree_cv.fit(X,y)
# Print the tuned parameters and score
print("Tuned Decision Tree Parameters: {}".format(tree_cv.best_params_))
print("Best score is {}".format(tree_cv.best_score_))
#Tuned Decision Tree Parameters: {'criterion': 'gini', 'max_depth': 3, 'max_features': 5, 'min_samples_leaf': 2}
#Best score is 0.7395833333333334
# RandomizedSearchCV will never outperform GridSearchCV. Instead, it is valuable because it saves on computation time.
####################33
# First: Hold-out evaluation data
# how well can the model perform on never seen data?
#random grid
random_grid = {
'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf
}
#Random forest regressor with random grid
rf = RandomForestRegressor()
rf_random = RandomizedSearchCV(estimator=rf,
param_distributions=random_grid,
n_iter=100,
scoring='neg_mean_squared_error',
verbose=2,
random_state=7,
cv=5)
#timer function
def timer(start_time=None):
if not start_time:
start_time = datetime.now()
return start_time
elif start_time:
hour, temp_sec = divmod((datetime.now() - start_time).total_seconds(),
3600)
mins, sec = divmod(temp_sec, 60)
print("\n Time taken: %i:%i:%s" % (hour, mins, round(sec, 2)))
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV # Create the parameter grid based on the results of random search
param_grid = {
'bootstrap': [True],
'max_depth': [80, 90, 100, 110],
'max_features': [2, 3],
'min_samples_leaf': [3, 4, 5],
'min_samples_split': [8, 10, 12],
'n_estimators': [100, 200, 300, 500, 1000]
}
# Create a based model
rf = RandomForestRegressor() # Instantiate the grid search model
random_search_rf = RandomizedSearchCV(estimator=rf,
param_distributions=param_grid,
cv=10,
n_jobs=-1,
verbose=2)
model_fit_RF = random_search_rf.fit(X_train, y_train)
print(model_fit_RF.best_params_)
##Testing the model
test_predict_RF = model_fit_RF.predict(X_test)
from sklearn import metrics
print('RF Mean Absolute Error:',
metrics.mean_absolute_error(y_test, test_predict_RF))
print('RF Mean Squared Error:',
metrics.mean_squared_error(y_test, test_predict_RF))
print('RF R2:', metrics.r2_score(y_test, test_predict_RF))
print('RF Root Mean Squared Error:',
def ml_tests(imputed_data):
# ScikitLearn Anforderung: Nur numerische Werte - Transformation der kategorischen Spalten
categorical_mask = (imputed_data.dtypes == "category")
categorical_columns = imputed_data.columns[categorical_mask].tolist()
category_enc = pd.get_dummies(imputed_data[categorical_columns])
imputed_data = pd.concat([imputed_data, category_enc], axis=1)
imputed_data = imputed_data.drop(columns=categorical_columns)
imputed_data = imputed_data.reset_index()
# Ausgabe
# print(imputed_data.info())
# imputed_data.to_excel(excel_writer="Files/Tests/imputed_data.xlsx", sheet_name="Immobilien")
# XGBoost Standardmodell
print("XGBoost Standardmodell:")
x = imputed_data.drop(columns=["angebotspreis"]).values
y = imputed_data["angebotspreis"].values
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
xg_reg = xgb.XGBRegressor(objective="reg:squarederror",
n_estimators=20,
seed=123)
xg_reg.fit(x_train, y_train)
preds = xg_reg.predict(x_test)
rmse = np.sqrt(mean_squared_error(y_test, preds))
print("RMSE: %f" % rmse)
print()
print_feature_importances(
model=xg_reg, data=imputed_data.drop(columns=["angebotspreis"]))
# Grid Search parameter Tuning
print("Grid Search Parameter Tuning:")
gbm_param_grid = {
'colsample_bytree': [0.3, 0.7],
'n_estimators': [50],
'max_depth': [2, 5]
}
gbm = xgb.XGBRegressor(objective="reg:squarederror")
grid_mse = GridSearchCV(estimator=gbm,
param_grid=gbm_param_grid,
scoring="neg_mean_squared_error",
cv=4,
verbose=1)
grid_mse.fit(x_train, y_train)
print("Best parameters found: ", grid_mse.best_params_)
print("Lowest RMSE Grid Search found: ",
np.sqrt(np.abs(grid_mse.best_score_)))
print()
# Randomized Search parameter tuning
print("Randomized Search Parameter Tuning:")
gbm_param_grid2 = {'n_estimators': [25], 'max_depth': range(2, 12)}
gbm2 = xgb.XGBRegressor(objective="reg:squarederror", n_estimators=10)
randomized_mse = RandomizedSearchCV(estimator=gbm2,
param_distributions=gbm_param_grid2,
scoring="neg_mean_squared_error",
n_iter=5,
cv=4,
verbose=1)
randomized_mse.fit(x_train, y_train)
print("Best parameters found: ", randomized_mse.best_params_)
print("Lowest RMSE Randomized Search found: ",
np.sqrt(np.abs(randomized_mse.best_score_)))
dm_train = xgb.DMatrix(data=x_train, label=y_train)
dm_test = xgb.DMatrix(data=x_test, label=y_test)
params = {"booster": "gblinear", "objective": "reg:squarederror"}
xg_reg2 = xgb.train(dtrain=dm_train, params=params, num_boost_round=15)
preds2 = xg_reg2.predict(dm_test)
rmse = np.sqrt(mean_squared_error(y_test, preds2))
print("RMSE: %f" % rmse)
reg_params = [0.1, 0.3, 0.7, 1, 10, 100]
params1 = {"objective": "reg:squarederror", "max_depth": 3}
rmses_l2 = []
for reg in reg_params:
params1["lambda"] = reg
cv_results_rmse = xgb.cv(dtrain=dm_train,
params=params1,
nfold=3,
num_boost_round=15,
metrics="rmse",
as_pandas=True)
rmses_l2.append(cv_results_rmse["test-rmse-mean"].tail(1).values[0])
print("Best rmse as a function of l2:")
print(pd.DataFrame(list(zip(reg_params, rmses_l2)), columns=["l2",
"rmse"]))
print()
print_feature_importances(
model=xg_reg2, data=imputed_data.drop(columns=["angebotspreis"]))
# Stochastic Gradient Boosting
print("Stochastic Gradient Boosting:")
sgbr = GradientBoostingRegressor(max_depth=4,
subsample=0.9,
max_features=0.75,
n_estimators=200,
random_state=2)
sgbr.fit(x_train, y_train)
y_pred = sgbr.predict(x_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print("RMSE: %f" % rmse)
print()
print_feature_importances(
model=sgbr, data=imputed_data.drop(columns=["angebotspreis"]))
# Random Forrest
print("Random Forrest:")
rf = RandomForestRegressor(n_estimators=25, random_state=2)
rf.fit(x_train, y_train)
y_pred2 = rf.predict(x_test)
rmse = np.sqrt(mean_squared_error(y_test, y_pred2))
print("RMSE: %f" % rmse)
print()
print_feature_importances(
model=rf, data=imputed_data.drop(columns=["angebotspreis"]))
n_estimators = [1000]
max_features = ['auto']
max_depth = [3, 5, 7]
min_samples_leaf = [2, 4]
random_grid = {'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_leaf': min_samples_leaf
}
rf = RandomForestClassifier()
rf_random = RandomizedSearchCV(
estimator=rf,
param_distributions=random_grid,
n_iter=100,
cv=3,
scoring='accuracy',
verbose=5,
n_jobs=-1)
rf_random.fit(X_train, y_train)
print(rf_random.best_params_)
print("Scores for the Train Dataset: ")
y_train_pred = rf_random.predict(X_train)
accuracy_train = accuracy_score(y_train, y_train_pred)
print("Accuracy: %.2f%%" % (accuracy_train * 100.0))
print("- - - - - - - - - - ")
print("Scores for the Test Dataset: ")
np.random.seed(SEED)
param_space = {
"max_depth": [3, 5, 10, 15, 20, 30, None],
"min_samples_split" : randint(2, 150),
"min_samples_leaf" : randint(2, 150),
"criterion" : ["gini", "entropy"]
}
raw_train_x, validation_x, raw_train_y, validation_y = train_test_split(x, y, test_size=0.25, random_state=SEED, stratify=y)
from sklearn.model_selection import RandomizedSearchCV
search = RandomizedSearchCV(DecisionTreeClassifier(),
param_space,
n_iter=100,
cv = 5,
random_state = SEED)
search.fit(raw_train_x, raw_train_y)
results = pd.DataFrame(search.cv_results_)
results.head()
print(len(results))
print(search.best_params_)
print(search.best_score_)
best = search.best_estimator_
best
predictions = best.predict(validation_x)
loss = ['deviance', 'exponential']
grid_random = {
'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'loss': loss
}
# random search of parameters, parameters of the randomnized search can be modified
GB_random = RandomizedSearchCV(estimator=GD_Classifier,
param_distributions=grid_random,
n_iter=20,
cv=3,
verbose=2,
random_state=42,
n_jobs=-1)
# fit the randomnized search CV
GB_random.fit(X_train, Y_train)
print(
'>>> Starting Grid Search Cross Validation to find optimal Gradient Boosting Classfifier...'
)
# create the parameter grid based on the results of random search
# varying slightly the optimised parameters found
grid_parameters = {
'loss': [GB_random.best_params_['loss']],
'max_leaf_nodes': (1,10,100,),
'min_samples_split': (0.1,0.25,0.5,0.75,1.0,),
'min_samples_leaf': (1,10,100,),
}]
#est=ensemble.RandomForestRegressor()
#est=kernel_ridge.KernelRidge()
#est=neighbors.NearestNeighbors()
#est=neighbors.KNeighborsRegressor()
est=ensemble.ExtraTreesRegressor()
# https://stackoverflow.com/questions/37161563/how-to-graph-grid-scores-from-gridsearchcv
# run randomized search
n_iter_search = 20
rs = RandomizedSearchCV(est, param_distributions=hyper_params, n_iter=n_iter_search)
t0 = time.time()
rs.fit(x_train, y_train.ravel())
runtime = time.time() - t0
print("RandomizedSearchCV took %.6f seconds for %d candidates" " parameter settings." % (runtime, n_iter_search))
print(rs.cv_results_)
#scores = [x[1] for x in rs.grid_scores_]
#scores = np.array(scores).reshape(len(Cs), len(Gammas))
#
#for ind, i in enumerate(Cs):
# plt.plot(Gammas, scores[ind], label='C: ' + str(i))
#plt.legend()
#plt.xlabel('Gamma')
#plt.ylabel('Mean score')
#plt.show()
from sklearn.model_selection import ShuffleSplit
clf_xgb = xgb.XGBClassifier(objective = 'binary:logistic')
param_dist = {'silent': [False],
'max_depth': [6, 10, 15, 20],
'learning_rate': [0.001, 0.01, 0.1, 0.2, .3, .4, 0,3],
'subsample': [0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
'colsample_bytree': [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
'colsample_bylevel': [0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
'min_child_weight': [0.5, 1.0, 3.0, 5.0, 7.0, 10.0],
'gamma': [0, 0.25, 0.3, 0.4, 0.5, 1.0],
'reg_lambda': [0.1, 1.0, 5.0, 10.0, 50.0, 100.0],
'reg_alpha': 2. ** np.arange(-13, 10, 2),
'n_estimators': [100,150,200]
}
clf = RandomizedSearchCV(clf_xgb, param_distributions = param_dist,
n_iter = 25,
scoring = 'f1', error_score = 0,
verbose = 3, n_jobs = -1)
rs = ShuffleSplit(n_splits=3, test_size=.25, random_state=0)
gc.collect()
estimators = []
results = np.zeros(len(one_hot_encoded_X))
score = []
for train_index, test_index in rs.split(one_hot_encoded_X):
# print('Iteration:', i)
X_train, X_test = one_hot_encoded_X.iloc[train_index], one_hot_encoded_X.iloc[test_index]
y_train, y_test = new_y_binary.iloc[train_index], new_y_binary.iloc[test_index]
clf.fit(X_train, y_train)
estimators.append(clf.best_estimator_)
'scaler': scalers_to_test,
'red_dim': [PCA()],
'red_dim__n_components': n_features_to_test,
'clf__hidden_layer_sizes': l,
'clf__activation': ['identity', 'logistic', 'tanh', 'relu'],
'clf__solver': ['lbfgs', 'sgd', 'adam'],
'clf__batch_size': b_size,
'clf__learning_rate': ['constant', 'invscaling', 'adaptive']
}]
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
grid = RandomizedSearchCV(pipeline,
param_distributions=parameteres,
n_iter=100,
cv=5,
random_state=1)
print('a')
grid.fit(X_train, y_train)
print('b')
score = {grid.score(X_test, y_test)}
score = grid.score(X_test, y_test)
best_p = grid.best_params_
#print(f'score = {grid.score(X_test, y_test)}')
#print(grid.best_params_)
# Define the parameters that will be tuned randomly
keras_param_options = {'filters' : [4, 8, 16],
'filters_LSTM' : [4, 8, 16],
'strides' : [1],
'padding' : ['valid'],
'activation_convolution' : [None],
'activation_LSTM' : ['tanh'],
'optimizers' : ['Adam', 'Adadelta'],
'number_hidden_units' : [4, 8],
'epochs' : [30],
'batch_size' : [8, 16, 32]}
# Using RandomizedSearchCV to find the best model randomly
random_search = RandomizedSearchCV(model,
param_distributions = keras_param_options,
n_iter = 50,
cv = 5,
verbose = 10)
# Fit to the training data
random_search.fit(x_train, y_train)
df_result_hyper_tuned = pd.DataFrame.from_dict(random_search.cv_results_)
df_result_hyper_tuned.to_csv('/hpc-home/kristian/effector-non-effector/scripts-cnn-lstm-separate-group/scripts-scan-multiclass/bacteria/results/all_scan_results_cnn_lstm_scan_bacteria_secreted.csv')
# Save all of the params to be used to predict on the test data
df_result_hyper_tuned['mean_test_score']= pd.to_numeric(df_result_hyper_tuned['mean_test_score'])
param_best_model_dict = dict(df_result_hyper_tuned.nlargest(30, 'mean_test_score')['params'])
params = list(param_best_model_dict.values())
print(params)
# Get info ahead about the best model obtained
y_dat = data['cancel_1.0']
X_dat = data.drop(['id','train','credit','state','cancel_1.0'], axis=1)
X_train, X_test, y_train,y_test = cross_validation.train_test_split(X_dat, y_dat, test_size=0.2, random_state=0)
params = {
'min_child_weight': [1, 5, 10],
'gamma': [0.5, 1, 1.5, 2, 5],
'subsample': [0.6, 0.8, 1.0],
'colsample_bytree': [0.6, 0.8, 1.0],
'max_depth': [3, 4, 5]
}
# The data is huge, so maybe don't need so many cv
folds = 3
# Test Enough of these
param_comb = 1
skf = StratifiedKFold(n_splits=folds, shuffle = True, random_state = 1001)
# early stopping to decrease time
xgb = XGBClassifier(learning_rate=0.02, n_estimators=100, objective='binary:logistic', silent=True, nthread=1)
random_search = RandomizedSearchCV(xgb, param_distributions=params, n_iter=param_comb, scoring='roc_auc', n_jobs=8, cv=skf.split(X_train,y_train), verbose=3, random_state=1001 )
start_time = timer(None) # timing starts from this point for "start_time" variable
random_search.fit(X_train, y_train)
timer(start_time) # timing ends here for "start_time" variable
results = pd.DataFrame(random_search.cv_results_)
results.to_csv('xgb-random-grid-search-results-01.csv', index=False)
"batch_size": batches,
'opt': optimizer,
"drop": droptout,
'lr': lr,
'act': act,
'epochs': epoch,
'validation_split': splt
}
model = KerasClassifier(build_fn=build_model, verbose=1)
hyperparameters = create_hyper()
search = RandomizedSearchCV(estimator=model,
param_distributions=hyperparameters,
n_iter=1,
cv=None,
n_jobs=1)
search.fit(x_train, y_train)
# print(search.estimator.fit(x_test,y_test))
pred = search.predict(x_test)
print(pred)
print(y_train)
print("shape", y_test.shape)
print("shape", pred.shape)
try:
pred = np.argmax(pred)
score = accuracy_score(y_test, pred)
print(score)
#predict_train=clf.predict(X_train)
pred_tree_test=decisionTree.predict(X_test)
pred_tree_train=decisionTree.predict(X_train)
tree_accuracy_train=model_generate_reports(y_train,pred_tree_train)
tree_accuracy_test=model_generate_reports(y_test,pred_tree_test)
from sklearn.metrics import confusion_matrix
confusion_matrixTree = confusion_matrix(y_test, pred_tree_test)
## randomized search cv for decision tree
param_dist = {"max_depth": [2,3,4,5,6,7,8,9, None],
"max_features": [2,4,6,8,10,12,14,16,18,20],
"min_samples_leaf":[2,4,6,8,10,12,14,16,18,20,22,24,26],
"criterion": ["gini", "entropy"]}
tree_cv = RandomizedSearchCV(decisionTree, param_dist, cv = 5)
tree_cv.fit(X_train,y_train)
print("Tuned Decision Tree Parameters: {}".format(tree_cv.best_params_))
pred_tree_train=tree_cv.predict(X_train)
pred_tree_test=tree_cv.predict(X_test)
tree_accuracy_train=model_generate_reports(y_train,pred_tree_train)
tree_accuracy_test=model_generate_reports(y_test,pred_tree_test)
confusion_matrixTree=confusion_matrix(y_test, pred_tree_test)
############## final decision tree#################################
tree_algo=DecisionTreeClassifier(random_state=0,min_samples_leaf=10,max_features=20,max_depth=9,criterion='entropy')
tree_algo.fit(X_train,y_train)
#predict_train=clf.predict(X_train)
pred_tree_test=tree_algo.predict(X_test)
pred_tree_train=tree_algo.predict(X_train)
tree_accuracy_train=model_generate_reports(y_train,pred_tree_train)
'learning_rate': [0.1],
'max_depth': [6],
'booster': ['dart'],
'rate_drop': [0.21],
'eval_metric': ['logloss', 'mae'],
'is_training_metric': [True],
'max_leaves': [144],
'colsample_bytree': [0.8],
'subsample': [0.8],
'seed': [66]
}]
kfold = KFold(n_splits=5, shuffle=True, random_state=66)
y_test_pred = []
y_pred = []
search = RandomizedSearchCV(XGBRegressor(n_jobs=6),
parameters,
cv=kfold,
n_iter=1)
for i in range(4):
fit_params = {
'verbose': True,
'eval_metric': ['logloss', 'mae'],
'eval_set': [(x_train, y_train[:, i]), (x_test, y_test[:, i])],
'early_stopping_rounds': 5
}
search.fit(x_train, y_train[:, i], **fit_params)
y_pred.append(search.predict(x_pred))
y_test_pred.append(search.predict(x_test))
# print(search.best_score_)
#############################
class Trainer(object):
# Mlflow parameters identifying the experiment, you can add all the parameters you wish
ESTIMATOR = "Linear"
EXPERIMENT_NAME = "TaxifareModel"
def __init__(self, X, y, **kwargs):
"""
FYI:
__init__ is called every time you instatiate Trainer
Consider kwargs as a dict containig all possible parameters given to your constructor
Example:
TT = Trainer(nrows=1000, estimator="Linear")
==> kwargs = {"nrows": 1000,
"estimator": "Linear"}
:param X:
:param y:
:param kwargs:
"""
self.pipeline = None
self.kwargs = kwargs
self.grid = kwargs.get("gridsearch", False) # apply gridsearch if True
self.local = kwargs.get("local",
True) # if True training is done locally
self.optimize = kwargs.get(
"optimize",
False) # Optimizes size of Training Data if set to True
self.mlflow = kwargs.get("mlflow", False) # if True log info to nlflow
self.upload = kwargs.get("upload", False) # if True log info to nlflow
self.experiment_name = kwargs.get("experiment_name",
self.EXPERIMENT_NAME) # cf doc above
self.model_params = None # for
self.X_train = X
self.y_train = y
del X, y
self.split = self.kwargs.get("split", True) # cf doc above
if self.split:
self.X_train, self.X_val, self.y_train, self.y_val = train_test_split(
self.X_train, self.y_train, test_size=0.15)
self.nrows = self.X_train.shape[0] # nb of rows to train on
self.log_kwargs_params()
self.log_machine_specs()
def get_estimator(self):
estimator = self.kwargs.get("estimator", self.ESTIMATOR)
if estimator == "Lasso":
model = Lasso()
elif estimator == "Ridge":
model = Ridge()
elif estimator == "Linear":
model = LinearRegression()
elif estimator == "GBM":
model = GradientBoostingRegressor()
elif estimator == "RandomForest":
model = RandomForestRegressor()
self.model_params = { # 'n_estimators': [int(x) for x in np.linspace(start = 50, stop = 200, num = 10)],
'max_features': ['auto', 'sqrt']
}
# 'max_depth' : [int(x) for x in np.linspace(10, 110, num = 11)]}
elif estimator == "xgboost":
model = XGBRegressor(objective='reg:squarederror',
n_jobs=-1,
max_depth=10,
learning_rate=0.05,
gamma=3)
self.model_params = {
'max_depth': range(10, 20, 2),
'n_estimators': range(60, 220, 40),
'learning_rate': [0.1, 0.01, 0.05]
}
else:
model = Lasso()
estimator_params = self.kwargs.get("estimator_params", {})
self.mlflow_log_param("estimator", estimator)
model.set_params(**estimator_params)
print(colored(model.__class__.__name__, "red"))
return model
def set_pipeline(self):
memory = self.kwargs.get("pipeline_memory", None)
dist = self.kwargs.get("distance_type", "euclidian")
feateng_steps = self.kwargs.get(
"feateng",
["distance", "time_features", 'direction', 'distance_to_center'])
if memory:
memory = mkdtemp()
# Define feature engineering pipeline blocks here
pipe_time_features = make_pipeline(
TimeFeaturesEncoder(time_column='pickup_datetime'),
OneHotEncoder(handle_unknown='ignore'))
pipe_distance = make_pipeline(
DistanceTransformer(distance_type=dist, **DIST_ARGS),
RobustScaler())
pipe_geohash = make_pipeline(AddGeohash(), ce.HashingEncoder())
pipe_direction = make_pipeline(Direction(), RobustScaler())
pipe_distance_to_center = make_pipeline(DistanceToCenter(),
RobustScaler())
# Define default feature engineering blocs
feateng_blocks = [
('distance', pipe_distance, list(DIST_ARGS.values())),
('time_features', pipe_time_features, ['pickup_datetime']),
('geohash', pipe_geohash, list(DIST_ARGS.values())),
('direction', pipe_direction, list(DIST_ARGS.values())),
('distance_to_center', pipe_distance_to_center,
list(DIST_ARGS.values())),
]
# Filter out some bocks according to input parameters
for bloc in feateng_blocks:
if bloc[0] not in feateng_steps:
feateng_blocks.remove(bloc)
features_encoder = ColumnTransformer(feateng_blocks,
n_jobs=None,
remainder="drop")
self.pipeline = Pipeline(steps=[('features', features_encoder),
('rgs', self.get_estimator())],
memory=memory)
if self.optimize:
self.pipeline.steps.insert(
-1,
['optimize_size', OptimizeSize(verbose=False)])
def add_grid_search(self):
""""
Apply Gridsearch on self.params defined in get_estimator
{'rgs__n_estimators': [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)],
'rgs__max_features' : ['auto', 'sqrt'],
'rgs__max_depth' : [int(x) for x in np.linspace(10, 110, num = 11)]}
"""
# Here to apply ramdom search to pipeline, need to follow naming "rgs__paramname"
params = {"rgs__" + k: v for k, v in self.model_params.items()}
self.pipeline = RandomizedSearchCV(estimator=self.pipeline,
param_distributions=params,
n_iter=10,
cv=2,
verbose=1,
random_state=42,
n_jobs=None)
@simple_time_tracker
def train(self, gridsearch=False):
tic = time.time()
self.set_pipeline()
if gridsearch:
self.add_grid_search()
self.pipeline.fit(self.X_train, self.y_train)
# mlflow logs
self.mlflow_log_metric("train_time", int(time.time() - tic))
def evaluate(self):
rmse_train = self.compute_rmse(self.X_train, self.y_train)
self.mlflow_log_metric("rmse_train", rmse_train)
if self.split:
rmse_val = self.compute_rmse(self.X_val, self.y_val, show=True)
self.mlflow_log_metric("rmse_val", rmse_val)
print(
colored(
"rmse train: {} || rmse val: {}".format(
rmse_train, rmse_val), "blue"))
else:
print(colored("rmse train: {}".format(rmse_train), "blue"))
def compute_rmse(self, X_test, y_test, show=False):
if self.pipeline is None:
raise ("Cannot evaluate an empty pipeline")
y_pred = self.pipeline.predict(X_test)
if show:
res = pd.DataFrame(y_test)
res["pred"] = y_pred
print(colored(res.sample(5), "blue"))
rmse = compute_rmse(y_pred, y_test)
return round(rmse, 3)
def save_model(self):
"""Save the model into a .joblib and upload it on Google Storage /models folder
HINTS : use sklearn.joblib (or jbolib) libraries and google-cloud-storage"""
joblib.dump(self.pipeline, 'model.joblib')
print(colored("model.joblib saved locally", "green"))
if self.upload:
storage_upload(model_version=MODEL_VERSION)
### MLFlow methods
@memoized_property
def mlflow_client(self):
mlflow.set_tracking_uri(MLFLOW_URI)
return MlflowClient()
@memoized_property
def mlflow_experiment_id(self):
try:
return self.mlflow_client.create_experiment(self.experiment_name)
except BaseException:
return self.mlflow_client.get_experiment_by_name(
self.experiment_name).experiment_id
@memoized_property
def mlflow_run(self):
return self.mlflow_client.create_run(self.mlflow_experiment_id)
def mlflow_log_param(self, key, value):
if self.mlflow:
self.mlflow_client.log_param(self.mlflow_run.info.run_id, key,
value)
def mlflow_log_metric(self, key, value):
if self.mlflow:
self.mlflow_client.log_metric(self.mlflow_run.info.run_id, key,
value)
def log_estimator_params(self):
reg = self.get_estimator()
self.mlflow_log_param('estimator_name', reg.__class__.__name__)
params = reg.get_params()
for k, v in params.items():
self.mlflow_log_param(k, v)
def log_kwargs_params(self):
if self.mlflow:
for k, v in self.kwargs.items():
self.mlflow_log_param(k, v)
def log_machine_specs(self):
cpus = multiprocessing.cpu_count()
mem = virtual_memory()
ram = int(mem.total / 1000000000)
self.mlflow_log_param("ram", ram)
self.mlflow_log_param("cpus", cpus)
def do_fold(j):
print("\tFold " + str(j+1))
train_idx = folds_i[j][0]
valid_idx = folds_i[j][1]
training_fold = developement_df.loc[train_idx, ]
training_fold = training_fold.reset_index(drop=True)
validation_fold = developement_df.loc[valid_idx, ]
validation_fold = validation_fold.reset_index(drop=True)
# shuffle the folds
training_stats_i_f = Statistics()
validation_stats_i_f = Statistics()
testing_stats_i_f = Statistics()
# Init the label ranking lists.
label_pred_proba_train = []
label_pred_proba_valid = []
label_pred_proba_test = []
label_y_train = []
label_y_valid = []
label_y_test = []
# Set up the vectorizer for the bag-of-words representation
if vectorizer_method == 'tf-idf':
vectorizer = TfidfVectorizer(
stop_words=['go', '', ' '], binary=binary, lowercase=True,
sublinear_tf=True, max_df=1.0, min_df=0
)
vectorizer.fit(training_fold['terms'].values)
alpha = None
percentile = 100
elif vectorizer_method == 'count':
vectorizer = CountVectorizer(
stop_words=['go', '', ' '], binary=binary, lowercase=True
)
vectorizer.fit(training_fold['terms'].values)
alpha = None
percentile = 100
else:
raise TypeError("Vectorizer_method has type {}.".format(type(vectorizer_method)))
selectors = generate_selectors(selection, vectorizer.get_feature_names(), dag)
base_estimators = make_classifiers(method, balanced, labels, selectors, selection, rng)
for label in sorted(labels):
print("\t\tFitting for label {}...".format(label))
# SVMs make the assumption of standardised features. Hence we scale the features
# avoiding the use of mean to maintain the structure of count sparsity. Scaling
# May also help with linear model convergence speed.
x_train_l = vectorizer.transform(training_fold['terms'].values)
y_train_l = np.asarray(training_fold[label].values, dtype=int)
x_valid_l = vectorizer.transform(validation_fold['terms'].values)
y_valid_l = np.asarray(validation_fold[label].values, dtype=int)
x_test_l = vectorizer.transform(testing_df['terms'].values)
y_test_l = np.asarray(test_df_i[label].values, dtype=int)
if scale:
x_train_l = mean_center(x_train_l, with_mean=False)
x_valid_l = mean_center(x_valid_l, with_mean=False)
x_test_l = mean_center(x_test_l, with_mean=False)
# We generate the folds for randomised search up-front. We hold out one of the folds for
# Probability calibration so each sampled param set gets calibrated on the same data.
# This leaves cv_folds-2 folds for randomised search cross-validation.
# cv_rand = StratifiedKFold(n_splits=3, shuffle=True, random_state=rng)
base_estimator_l = base_estimators[label]
fresh_estimator = clone(base_estimator_l)
# Find the best params, then do a final proper calibration.
params = sk_generate_params(method, selection)
estimator_l = RandomizedSearchCV(
estimator=base_estimator_l, param_distributions=params,
n_iter=60, scoring='f1', cv=3, random_state=rng,
error_score=0.0, n_jobs=1, pre_dispatch='2*n_jobs',
refit=True
)
# Test if there's any signal if we permute the labels.
# Classifier should do poorly if we do so.
if permute:
y_train_l = rng.permutation(y_train_l)
threshold = 0.5
estimator_l.fit(x_train_l, y_train_l)
best_params_l = estimator_l.best_params_
# Calibrate the random forest with the best hyperparameters.
if method not in ['lr']:
estimator_l = CalibratedClassifierCV(fresh_estimator.set_params(**best_params_l),
cv=3, method='sigmoid')
estimator_l.fit(x_train_l, y_train_l)
# Evaluate Performance characteristics and test on training to check overfitting.
y_train_prob_l = estimator_l.predict_proba(x_train_l)
y_valid_prob_l = estimator_l.predict_proba(x_valid_l)
y_test_prob_l = estimator_l.predict_proba(x_test_l)
training_stats_i_f.merge(evaluate_model(y_train_l, y_train_prob_l, label, threshold))
validation_stats_i_f.merge(evaluate_model(y_valid_l, y_valid_prob_l, label,threshold))
# Compute independent test data performance
testing_stats_i_f.merge(evaluate_model(y_test_l, y_test_prob_l, label, threshold))
# Get label ranking info
label_pred_proba_train.append([p[1] for p in y_train_prob_l])
label_pred_proba_valid.append([p[1] for p in y_valid_prob_l])
label_pred_proba_test.append([p[1] for p in y_test_prob_l])
label_y_train.append(y_train_l)
label_y_valid.append(y_valid_l)
label_y_test.append(y_test_l)
print(validation_stats_i_f.frame())
# Compute multi-label performance statistics
y = np.vstack(zip(*label_y_train))
y_prob = np.vstack(zip(*label_pred_proba_train))
training_stats_i_f.merge(multi_label_evaluate(y, y_prob, threshold))
y = np.vstack(zip(*label_y_valid))
y_prob = np.vstack(zip(*label_pred_proba_valid))
validation_stats_i_f.merge(multi_label_evaluate(y, y_prob, threshold))
y = np.vstack(zip(*label_y_test))
y_prob = np.vstack(zip(*label_pred_proba_test))
testing_stats_i_f.merge(multi_label_evaluate(y, y_prob, threshold))
return training_stats_i_f, validation_stats_i_f, testing_stats_i_f
bootstrap = [True, False]
# Create the random grid
random_grid = {
'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'bootstrap': bootstrap
}
pprint(random_grid)
# search across 100 different combinations, and use all available cores
rf_random = RandomizedSearchCV(estimator=rfc,
param_distributions=random_grid,
n_iter=100,
cv=kfold,
verbose=2,
random_state=42,
n_jobs=-1)
rf_random.fit(x_train, y_train)
rf_random.best_params_
rf_best_random = rf_random.best_estimator_
prediction_RF = rf_best_random.predict(x_test)
print("for Random Forest we get " +
str(round(accuracy_score(y_test, prediction_RF), 5)))
CM_RF = confusion_matrix(y_test, prediction_RF)
df_cm = pd.DataFrame(CM_RF,
index=["Predicted No", "Predicted Yes"],
columns=["Actual No", "Actual Yes"])
plt.figure()
def tune_xgb_params_randomized(estimator_cls,
label: np.ndarray,
metric_sklearn: str,
n_jobs: int,
params: dict,
strat_folds: KFold,
train: np.ndarray,
n_iter: int = 20,
verbosity_level: int = 0,
**kwargs):
"""
:param estimator_cls:
The class type of the estimator to instantiate - either an XGBClassifier or an XGBRegressor.
:param label:
An array-like containing the labels of the classification or regression problem.
:param metric_sklearn:
The evaluation metric to be passed to scikit-learn's GridSearchCV - see
http://scikit-learn.org/stable/modules/model_evaluation.html
for the options this can take - e.g. 'neg_mean_squared_error' for RMSE.
:param n_jobs:
The number of jobs to run simultaneously.
:param params:
A dictionary of XGB parameters.
:param strat_folds:
A KFold object to cross validate the parameters.
:param train:
An array-like containing the training input samples.
:param n_iter:
An optional parameter to control the number of parameter settings that are sampled.
:param n_jobs:
An optional parameter to control the amount of parallel jobs - defaults to the amount of CPUs available.
:param verbosity_level:
An optional parameter to control the verbosity of the grid searching - defaults to the most verbose option.
:param kwargs:
Parameter distributions may be controlled through keyword arguments - e.g. to sample uniformly between 0.5 and 0.7 for
colsample_bytree, supply colsample_bytree_loc=0.5 and colsample_bytree_scale=0.2.
:return:
A dictionary of tuned parameters and a list of the parameters found at each step with their respective scores.
"""
params_copy = clean_params_for_sk(params)
param_distributions = {
'colsample_bytree': uniform(kwargs.get('colsample_bytree_loc', 0.2), kwargs.get('colsample_bytree_scale', 0.8)),
'gamma': uniform(kwargs.get('gamma_loc', 0), kwargs.get('gamma_scale', 0.9)),
'max_depth': sp_randint(kwargs.get('max_depth_low', 2), kwargs.get('max_depth_high', 11)),
'min_child_weight': sp_randint(kwargs.get('min_child_weight_low', 1), kwargs.get('min_child_weight_high', 11)),
'reg_alpha': halfnorm(kwargs.get('reg_alpha_loc', 0), kwargs.get('reg_alpha_scale', 5)),
'reg_lambda': halfnorm(kwargs.get('reg_alpha_loc', 0), kwargs.get('reg_alpha_scale', 5)),
'subsample': uniform(kwargs.get('subsample_loc', 0.2), kwargs.get('subsample_scale', 0.8))
}
rand_search = RandomizedSearchCV(
cv=strat_folds.split(train, label),
estimator=estimator_cls(**params_copy),
n_iter=n_iter,
n_jobs=n_jobs,
param_distributions=param_distributions,
scoring=metric_sklearn,
verbose=verbosity_level
)
rand_search.fit(train, label)
return rand_search.best_params_, [(rand_search.best_params_, rand_search.best_score_)]
outputs = Dense(10, activation='softmax', name='outputs')(x)
model = Model(inputs=inputs, outputs=outputs)
model.compile(optimizer=optimizer, metrics=['acc'], loss='categorical_crossentropy')
return model
def create_hyperparameters():
batchs = [10,20,30,40,50]
optimizers = ['rmsprop', 'adam', 'adadelta']
dropout = [0.1, 0.2, 0.3]
return{'batch_size' : batchs, 'optimizer': optimizers, 'drop':dropout}
hyperparameters = create_hyperparameters()
model2 = build_model()
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier #머신러닝이 케라스보다 더 먼저 나와서 랩핑을 해줘야 한다
model2 = KerasClassifier(build_fn=build_model, verbose=1)
from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
search = RandomizedSearchCV(model2, hyperparameters, cv=3) #cv cross validation
# search = GridSearchCV(model2, hyperparameters, cv=3) #cv cross validation
search.fit(x_train, y_train, verbose=1)
print(search.best_params_) #{'optimizer': 'rmsprop', 'drop': 0.1, 'batch_size': 50}
print(search.best_estimator_)
print(search.best_score_) #0.9588499863942465
acc = search.score(x_test, y_test)
print('최종 스코어 : ', acc ) #최종 스코어 : 0.9682999849319458
#{'optimizer': 'rmsprop', 'drop': 0.2, 'batch_size': 30} 고쳐도 된다
print("R-squared:", metrics.r2_score(y_test_pred, test_minmax[:, 1200]))
print("-----------------------------------------------------------")
print("-----------------------------------------------------------")
# ---------- HYPERPARAMETERS TUNING WITH RANDOM-SEARCH ------------
print("TUNED HYPERPARAMETERS WITH RANDOM-SEARCH")
print('-------------------------------------------------')
# KNN
param_dist = {'n_neighbors': sp_randint(2, 20)}
knnrs = RandomizedSearchCV(knn,
param_distributions=param_dist,
scoring='neg_mean_squared_error',
cv=tr_val_partition,
n_jobs=1,
verbose=1)
# Training the model with the random-search
np.random.seed(123)
knnrs.fit(train_closest, train_minmax[:, 1200])
# Making predictions on the testing partition
y_test_pred = knnrs.predict(test_closest)
# And finally computing the test accuracy
print("Mean squared error of KNN with tuned hyperparameters:",
metrics.mean_squared_error(y_test_pred, test_minmax[:, 1200]))
print("R-squared:", metrics.r2_score(y_test_pred, test_minmax[:, 1200]))
print("-----------------------------------------------------------")
def main():
""" Main function
"""
amigos_data = np.loadtxt('features_all_20s.csv',skiprows=1, delimiter=',')
labels = np.loadtxt('Final_Personality_20s.csv',skiprows=1, delimiter=',')
ids=np.loadtxt('ids_20s.csv',skiprows=1, delimiter=',')
labels=labels[:, 1]
kf = KFold(n_splits=2)
gkf=GroupKFold(n_splits=5)
# tune XGB classifier parameters
grid_search_params_xgb = {
'max_depth': [3,4,5],
'n_estimators': [10,15,20]
}
other_tuning_params_xgb = {
'learning_rate': np.arange(0.01, 0.41, 0.01),
'gamma': np.arange(0, 10.1, 0.5),
'min_child_weight': np.arange(0.80, 1.21, 0.01),
'max_delta_step': np.arange(0, 2.05, 0.05),
'subsample': np.arange(1.00, 0.59, -0.01),
'colsample_bytree': np.arange(1.00, 0.09, -0.01),
'colsample_bylevel': np.arange(1.00, 0.09, -0.01),
'reg_alpha': np.arange(0, 2.05, 0.05),
'reg_lambda': np.arange(0.50, 2.55, 0.05),
'scale_pos_weight': np.arange(0.80, 1.21, 0.01),
'base_score': np.arange(0.40, 0.61, 0.01),
'seed': np.arange(0, 41)
}
# XGB grid search tuning
best_params = {
'max_depth': 3,
'n_estimators': 20
}
acc = 0
print('Tuning max_depth and n_estimators')
for param in grid_search_params_xgb['max_depth']:
print('in grid search')
print('max_depth', param)
xgb_clf = {
'a': xgb.XGBClassifier(max_depth=param, objective="binary:logistic"),
}
tuning_params = grid_search_params_xgb['n_estimators']
param, tmp_acc = tuning(
xgb_clf, 'n_estimators', tuning_params, amigos_data, labels, kf)
print('param',param,'tmp_acc',tmp_acc)
if tmp_acc >= acc:
best_params['max_depth'] = param
best_params['n_estimators'] = param
acc = tmp_acc
# XGB tune other parameters
for param_name, tuning_params in other_tuning_params_xgb.items():
print('Tuning', param_name)
xgb_clf = {
'a': xgb.XGBClassifier(objective="binary:logistic"),
}
xgb_clf['a'].set_params(**best_params)
param,_ = tuning(
xgb_clf, param_name, tuning_params, amigos_data, labels, kf)
best_params[param_name] = param
# tune RF parameters
grid_search_params_rf = {
'max_features': [10,15,20],
'max_depth': [3,5,10],
}
rf_clf=RandomForestClassifier()
rf_random = RandomizedSearchCV(estimator = rf_clf, param_distributions = grid_search_params_rf, n_iter = 100, cv = gkf, verbose=2, random_state=42, n_jobs = 5)
rf_random.fit(amigos_data,np.ravel(labels),groups=ids)
#the optimized hyperparameters
print('XGBoost best parameters:', best_params)
print('Random forest best parameters:',rf_random.best_params_)
from sklearn.model_selection import RandomizedSearchCV
from sklearn.linear_model import SGDClassifier
from scipy.stats import lognorm as sp_lognormal
import cs231n.cifar10 as cf
np.random.seed(31337)
X_train, y_train, X_test, y_test, scaler = cf.get_normalised_data()
basic_svm = SGDClassifier(loss="hinge", penalty="l2", l1_ratio=0.0, random_state=31337, n_jobs=5)
# From the Scipy docs: to sample a random variable Y such that Y=exp(X) where X~N(mu,sigma), use
# scipy.stats.lognormal(s=sigma, scale=np.exp(mu))
random_search = RandomizedSearchCV(basic_svm,
param_distributions={'alpha': sp_lognormal(s=2, scale=np.exp(-4))},
n_iter=20, verbose=1)
random_search.fit(X_train, y_train)
print("Chosen: ", random_search.best_params_["alpha"])
print("Best CV score: ", random_search.best_score_)
chosen_svm = random_search.best_estimator_
os.makedirs("output/svc", exist_ok=True)
labels = cf.get_label_names()
for i in range(10):
# Don't forget to rescale the hyperplanes to get human-readable versions---the l2 penalty makes
# them close to the origin, so they look indistinguishable.
this_hyperplane = 127*(chosen_svm.coef_[i]/np.max(np.abs(chosen_svm.coef_[i]))) + scaler.mean_
cf.plot_image(this_hyperplane, "output/svc/archetype " + labels[i] + ".png")
#%%Ridge Regresion Model
model_name = "ridge_poly2"
X = train.drop(['energy'], axis=1)
cat_cols = ['hour', 'month', 'day_of_week']
cat_cols_idx = [X.columns.get_loc(c) for c in X.columns if c in cat_cols]
onehot = OneHotEncoder(categorical_features=cat_cols_idx, sparse=False)
regr = Ridge(fit_intercept=False)
poly = PolynomialFeatures(2)
tscv = TimeSeriesSplit(n_splits=3)
param_dist = {'alpha': st.uniform(1e-4, 5.0)}
regr_cv = RandomizedSearchCV(estimator=regr,
param_distributions=param_dist,
n_iter=20,
scoring='mean_squared_error',
iid=False,
cv=tscv,
verbose=2,
n_jobs=1)
regr_pipe = Pipeline([('onehot', onehot), ('poly', poly), ('regr_cv', regr_cv)])
regr_pipe.fit(X, y=train['energy'])
cv_results = pd.DataFrame(regr_pipe.named_steps['regr_cv'].cv_results_)
cv_results.sort_values(by='rank_test_score').head()
#%% Linear regression with recursive feature elimination
model_name = "linear_regression"
X = train.drop(['timeStamp','energy'], axis=1)
cat_cols = ['hour', 'month', 'day_of_week']
cat_cols_idx = [X.columns.get_loc(c) for c in X.columns if c in cat_cols]
onehot = OneHotEncoder(categorical_features=cat_cols_idx, sparse=False)
