def nearest_neighbors(self):
neighbors_array = [11, 31, 201, 401, 601]
tuned_parameters = {"n_neighbors" : neighbors_array}
knn = KNeighborsClassifier()
clf = GridSearchCV(knn, tuned_parameters, cv=5, n_jobs= 5, scoring = "f1")
clf.fit(self.train_data_x, self.train_labels_y)
self.models.append(clf)
def tuner(clf, parameters, data):
from sklearn.model_selection import GridSearchCV
labels, features = targetFeatureSplit(data)
scaler = MinMaxScaler()
select = SelectKBest()
steps = [("scale", scaler),
("select", select),
("classifier", clf)]
pipeline = Pipeline(steps)
shuffle = StratifiedShuffleSplit(n_splits=1000, test_size=0.3,
random_state=42)
my_scorer = make_scorer(my_score_func)
scoring_metric = my_scorer
grid_searcher = GridSearchCV(pipeline, param_grid=parameters,
cv=shuffle, scoring=scoring_metric)
features = select.fit_transform(features, labels)
grid_searcher.fit(features, labels)
print("Cross-validated {0} score: {1}".format(scoring_metric,
grid_searcher.best_score_))
print("Params: ", grid_searcher.best_params_)
def test_grid_search_precomputed_kernel():
# Test that grid search works when the input features are given in the
# form of a precomputed kernel matrix
X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)
# compute the training kernel matrix corresponding to the linear kernel
K_train = np.dot(X_[:180], X_[:180].T)
y_train = y_[:180]
clf = SVC(kernel='precomputed')
cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
cv.fit(K_train, y_train)
assert_true(cv.best_score_ >= 0)
# compute the test kernel matrix
K_test = np.dot(X_[180:], X_[:180].T)
y_test = y_[180:]
y_pred = cv.predict(K_test)
assert_true(np.mean(y_pred == y_test) >= 0)
# test error is raised when the precomputed kernel is not array-like
# or sparse
assert_raises(ValueError, cv.fit, K_train.tolist(), y_train)
def build_and_train():
data = pd.read_csv('../data/training.csv')
data = data.dropna(subset=['Gender', 'Married', 'Credit_History', 'LoanAmount'])
pred_var = ['Gender','Married','Dependents','Education','Self_Employed','ApplicantIncome','CoapplicantIncome',\
'LoanAmount','Loan_Amount_Term','Credit_History','Property_Area']
X_train, X_test, y_train, y_test = train_test_split(data[pred_var], data['Loan_Status'], \
test_size=0.25, random_state=42)
y_train = y_train.replace({'Y':1, 'N':0}).as_matrix()
y_test = y_test.replace({'Y':1, 'N':0}).as_matrix()
pipe = make_pipeline(PreProcessing(),
RandomForestClassifier())
param_grid = {"randomforestclassifier__n_estimators" : [10, 20, 30],
"randomforestclassifier__max_depth" : [None, 6, 8, 10],
"randomforestclassifier__max_leaf_nodes": [None, 5, 10, 20],
"randomforestclassifier__min_impurity_split": [0.1, 0.2, 0.3]}
grid = GridSearchCV(pipe, param_grid=param_grid, cv=3)
grid.fit(X_train, y_train)
return(grid)
def tune_parameters(features, labels):
"""
Use GridSearchCV to identify and return the best parameters to use
for the Decision Tree algorithm.
features = features list as returned by the targetFeatureSplit script
labels = target list as returned by the targetFeatureSplit script
"""
from sklearn import tree
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer
# Make scorer for the GridSearchCV function
scorer = make_scorer(custom_scorer, greater_is_better = True)
# Parameters names and settings to be used by GridSearchCV
parameters = [{"criterion": ["gini", "entropy"],
"splitter": ["best", "random"],
"min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10],
"min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
"min_impurity_split": [1e-9, 1e-8, 1e-7, 1e-6, 1e-5],
"presort": [True, False],
"random_state": [42]}]
# Use GridSearchCV to identify the best parameters
# K-fold cross-validation is used (100 folds)
# F1 score from custom_scorer function is used as the evaluator
clf = GridSearchCV(tree.DecisionTreeClassifier(), parameters, cv = 100, scoring = scorer)
clf.fit(features, labels)
best_parameters = clf.best_params_
return best_parameters
def test_ovo_gridsearch():
ovo = OneVsOneClassifier(LinearSVC(random_state=0))
Cs = [0.1, 0.5, 0.8]
cv = GridSearchCV(ovo, {'estimator__C': Cs})
cv.fit(iris.data, iris.target)
best_C = cv.best_estimator_.estimators_[0].C
assert_true(best_C in Cs)
def svm_hitters_params(to_predict_hitters, x_hitters, hitter_predictions):
# create lists of parameters to search through
c = [10**i for i in np.arange(-3,3)]
gamma = c
poly_coeff0 = [10**i for i in np.arange(0,3)]
# finding optimal parameters for svm
best_params = []
# preprocess the x values
x_hitters = preprocessing.scale(x_hitters)
for col in to_predict_hitters:
y = hitter_predictions[col].tolist()
x_train, x_test, y_train, y_test = train_test_split(x_hitters, y)
svr = svm.SVC()
parameters = {'kernel':['rbf'], 'gamma': gamma,'coef0': poly_coeff0}
clf = GridSearchCV(svr, parameters)
clf.fit(x_train, y_train)
best_params.append({col:clf.best_params_})
return best_params
def test_pipeline():
param_grid = [{'logisticregression__C': [1, 0.1, 10]}]
pipe = make_pipeline(StandardScaler(),
CopyTransformer(),
LogisticRegression())
grid = GridSearchCV(pipe, param_grid, cv=3, n_jobs=1)
grid.fit(X, y)
def test_stochastic_gradient_loss_param():
# Make sure the predict_proba works when loss is specified
# as one of the parameters in the param_grid.
param_grid = {
'loss': ['log'],
}
X = np.arange(24).reshape(6, -1)
y = [0, 0, 0, 1, 1, 1]
clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),
param_grid=param_grid)
# When the estimator is not fitted, `predict_proba` is not available as the
# loss is 'hinge'.
assert_false(hasattr(clf, "predict_proba"))
clf.fit(X, y)
clf.predict_proba(X)
clf.predict_log_proba(X)
# Make sure `predict_proba` is not available when setting loss=['hinge']
# in param_grid
param_grid = {
'loss': ['hinge'],
}
clf = GridSearchCV(estimator=SGDClassifier(loss='hinge'),
param_grid=param_grid)
assert_false(hasattr(clf, "predict_proba"))
clf.fit(X, y)
assert_false(hasattr(clf, "predict_proba"))
def plot_cross_val_selection():
iris = load_iris()
X_trainval, X_test, y_trainval, y_test = train_test_split(iris.data,
iris.target,
random_state=0)
param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],
'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}
grid_search = GridSearchCV(SVC(), param_grid, cv=5)
grid_search.fit(X_trainval, y_trainval)
scores = grid_search.grid_scores_[15:]
best = np.argmax([x.mean_validation_score for x in scores])
plt.figure(figsize=(10, 3))
plt.xlim(-1, len(scores))
plt.ylim(0, 1.1)
for i, score in enumerate(scores):
marker_cv, = plt.plot([i] * 5, score.cv_validation_scores, '^', c='gray', markersize=5, alpha=.5)
marker_mean, = plt.plot(i, score.mean_validation_score, 'v', c='none', alpha=1, markersize=10)
if i == best:
marker_best, = plt.plot(i, score.mean_validation_score, 'o', c='red', fillstyle="none", alpha=1, markersize=20, markeredgewidth=3)
plt.xticks(range(len(scores)), [str(score.parameters).strip("{}").replace("'", "") for score in scores], rotation=90);
plt.ylabel("validation accuracy")
plt.xlabel("parameter settings")
plt.legend([marker_cv, marker_mean, marker_best], ["cv accuracy", "mean accuracy", "best parameter setting"], loc=(1.05, .4))
def build(X, y=None):
"""
Inner build function that builds a single model.
:param X:
:param y:
:return:
"""
model = Pipeline([
('vectorizer', TfidfVectorizer(
tokenizer=self.spacy_tokenizer, preprocessor=None, lowercase=False)),
('clf', SVC(C=1,kernel="linear",
probability=True,
class_weight='balanced'))])
from sklearn.model_selection import GridSearchCV
items,counts= np.unique(y, return_counts=True)
cv_splits = max(2, min(5, np.min(counts) // 5))
Cs = [0.01,0.25,1, 2, 5, 10, 20, 100]
param_grid = {'clf__C': Cs, 'clf__kernel': ["linear"]}
grid_search = GridSearchCV(model,
param_grid=param_grid,
scoring='f1_weighted',
cv=cv_splits,
verbose=2,
n_jobs=-1
)
grid_search.fit(X, y)
return grid_search
def train_sgd(data, result, scoring=None):
print("train SGDClassifier {}".format(len(data)))
#scaler = None
#scaler = preprocessing.MinMaxScaler()
print("Scale: {}".format(type(scaler)))
if scaler != None:
data = scaler.fit_transform(data)
#classifier = SGDClassifier(loss="hinge", penalty="l2")
#classifier.fit(data, result)
#return scaler, classifier
parameters = {
'loss': ('hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron',
'squared_loss', 'huber', 'epsilon_insensitive',
'squared_epsilon_insensitive'),
'penalty': ('none', 'l2', 'l1', 'elasticnet')
}
print(parameters)
search = GridSearchCV(SGDClassifier(), parameters, scoring=scoring, n_jobs=1)
search.fit(data, result)
print("best params: {}".format(search.best_params_))
print("best score: {}".format(search.best_score_))
print
return scaler, search.best_estimator_.fit(data,result)
def fit(self, X, y, sample_weight=None, check_input=True):
"""Fit Ridge regression model after searching for the best mu and tau.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training data
y : array-like, shape = [n_samples] or [n_samples, n_targets]
Target values
sample_weight : float or array-like of shape [n_samples]
Sample weight
Returns
-------
self : Returns self.
"""
self._label_binarizer = LabelBinarizer(pos_label=1, neg_label=-1)
y = self._label_binarizer.fit_transform(y)
if self._label_binarizer.y_type_.startswith('multilabel'):
raise ValueError(
"%s doesn't support multi-label classification" % (
self.__class__.__name__))
else:
y = column_or_1d(y, warn=False)
param_grid = {'tau': self.taus, 'lamda': self.lamdas}
fit_params = {'sample_weight': sample_weight,
'check_input': check_input}
estimator = L1L2TwoStepClassifier(
mu=self.mu, fit_intercept=self.fit_intercept,
use_gpu=self.use_gpu, threshold=self.threshold,
normalize=self.normalize, precompute=self.precompute,
max_iter=self.max_iter,
copy_X=self.copy_X, tol=self.tol, warm_start=self.warm_start,
positive=self.positive,
random_state=self.random_state, selection=self.selection)
gs = GridSearchCV(
estimator=estimator,
param_grid=param_grid, fit_params=fit_params, cv=self.cv,
scoring=self.scoring, n_jobs=self.n_jobs, iid=self.iid,
refit=self.refit, verbose=self.verbose,
pre_dispatch=self.pre_dispatch, error_score=self.error_score,
return_train_score=self.return_train_score)
gs.fit(X, y)
estimator = gs.best_estimator_
self.tau_ = estimator.tau
self.lamda_ = estimator.lamda
self.coef_ = estimator.coef_
self.intercept_ = estimator.intercept_
self.best_estimator_ = estimator # XXX DEBUG
if self.classes_.shape[0] > 2:
ndim = self.classes_.shape[0]
else:
ndim = 1
self.coef_ = self.coef_.reshape(ndim, -1)
return self
def score_nestedCV(self, G1, model, param_grid, effect, nested):
k_fold = model_selection.KFold(n_splits=self.n_folds).split(range(self.Y.shape[0]))
i_fold=0
scores = sp.zeros(self.n_folds)
params = list()
for train, test in k_fold:
(trainData, trainY) = self._packData(G1, train, effect)
(testData, testY) = self._packData(G1, test, effect)
if nested:
clf = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs = self.n_jobs_grid,
cv=self.n_folds_params, scoring=self.scoring, verbose=self.verbose)
clf.fit(trainData, trainY.flatten())
params.append(clf.best_params_)
scores[i_fold] = clf.score(testData, testY.flatten(), method_scorer=False)
else:
model.fit(trainData, trainY.flatten())
scores[i_fold] = SCORERS[self.scoring](model, testData, testY.flatten())
i_fold+=1
return scores,params
def fit_branchmodel(self,xwl2,hm_y):
clf = LogisticRegression(C=1.,class_weight='balanced',penalty='l1',solver='liblinear',max_iter=300)
param_grid = dict(C=(np.logspace(-.2, 1, 15)))
#gridclf = GridSearchCV(clf, param_grid=param_grid, cv=StratifiedKFold(hm_y,n_folds=4), n_jobs=-1,scoring='precision_weighted')
gridclf = GridSearchCV(clf, param_grid=param_grid, cv=StratifiedShuffleSplit(n_splits=50, test_size=.2,random_state=1), n_jobs=-1,scoring='accuracy')
gridclf.fit(xwl2,hm_y)
return gridclf.best_estimator_
def optimize_model_regress(data, tc):
train_data = data.sample(frac=.8)
test_data = data.drop(train_data.index)
train_y = train_data['temperature']/tc
train_X = train_data.drop(['T/Tc','temperature'], axis=1)
test_y = test_data['temperature']/tc
test_X = test_data.drop(['T/Tc','temperature'], axis=1)
tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1,.5,.1,1e-2,1e-3, 1e-4],
'C': [.1,.5, 1,5, 10, 50, 100,500, 1000]},
{'kernel': ['linear'], 'C': [.1,.5, 1,5, 10, 50, 100,500, 1000]}]
model = GridSearchCV(svm.SVR(), tuned_parameters, cv=5)
model.fit(train_X, train_y)
print()
print("Best parameters:")
print()
print(model.best_params_)
print()
print("Grid scores:")
print()
means = model.cv_results_['mean_test_score']
stds = model.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, model.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r"
% (mean, std * 2, params))
print()
y_true, y_pred = test_y, model.predict(test_X)
print("Mean Absolute Error : " + str(mean_absolute_error(y_pred,y_true)))
print()
def model_selection(
x_matrix, y_vector, param_grid, cv=None, scoring=None):
pipeline = Pipeline(
[('resampler', None), ('classifier', DummyClassifier())])
grid_search_cv = GridSearchCV(pipeline, param_grid, cv=cv, scoring=scoring)
grid_search_cv.fit(x_matrix, y_vector)
return grid_search_cv
def inner_cv_loop(Xtrain,Ytrain,clf,parameters,
oversample=None,fa_dims=20,
verbose=False):
"""
use GridSearchCV to find best classifier for training set
"""
rocscore={}
best_est={}
facanal={}
for fa_d in [0,fa_dims]:
clfname='fa' if fa_d>0 else "nofa"
if fa_d>0:
facanal[clfname]=FactorAnalysis(fa_d)
Xtrain=facanal[clfname].fit_transform(Xtrain)
else:
facanal[clfname]=None
if verbose:
print(clfname)
gs=GridSearchCV(clf,parameters,scoring='roc_auc')
gs.fit(Xtrain,Ytrain)
rocscore[clfname]=gs.best_score_
best_est[clfname]=gs.best_estimator_
bestscore=numpy.max([rocscore[i] for i in rocscore.keys()])
bestclf=[i for i in rocscore.keys() if rocscore[i]==bestscore][0]
if verbose:
print('best:',bestclf,bestscore,best_est[bestclf],facanal[bestclf])
return best_est[bestclf],bestscore,facanal[bestclf]
def _search_param(self, metric, X, y):
'''
Find best potential parameters set using few n_estimators
'''
# Make sure user specified params are in the grid.
max_depth_grid = list(np.unique([self.model_instance.max_depth, 5, 7]))
colsample_bytree_grid = list(
np.unique([self.model_instance.colsample_bytree, 0.66, 0.9]))
reg_lambda_grid = list(np.unique([self.model_instance.reg_lambda, 1, 5]))
param_grid = {
'max_depth': max_depth_grid,
'learning_rate': [max(self.model_instance.learning_rate, 0.3)],
'n_estimators': [min(self.model_instance.n_estimators, 60)],
'gamma': [self.model_instance.gamma],
'min_child_weight': [self.model_instance.min_child_weight],
'max_delta_step': [self.model_instance.max_delta_step],
'subsample': [self.model_instance.subsample],
'colsample_bytree': colsample_bytree_grid,
'colsample_bylevel': [self.model_instance.colsample_bylevel],
'reg_alpha': [self.model_instance.reg_alpha],
'reg_lambda': reg_lambda_grid,
'scale_pos_weight': [self.model_instance.scale_pos_weight],
'base_score': [self.model_instance.base_score],
'seed': [self.model_instance.seed]
}
grid_search = GridSearchCV(
self.model_instance, param_grid, cv=2, refit=False, scoring=metric)
grid_search.fit(X, y)
best_params = grid_search.best_params_
# Change params back original params
best_params['learning_rate'] = self.model_instance.learning_rate
best_params['n_estimators'] = self.model_instance.n_estimators
return best_params
def test_gridsearch():
iris = load_iris()
X = iris.data
y = iris.target
knn = KNeighborsClassifier(n_neighbors=2)
sfs1 = SFS(estimator=knn,
k_features=3,
forward=True,
floating=False,
cv=5)
pipe = Pipeline([('sfs', sfs1),
('knn', knn)])
param_grid = [
{'sfs__k_features': [1, 2, 3, 4],
'sfs__estimator__n_neighbors': [1, 2, 3, 4]}
]
gs = GridSearchCV(estimator=pipe,
param_grid=param_grid,
n_jobs=1,
iid=False,
cv=5,
refit=False)
gs = gs.fit(X, y)
assert gs.best_params_['sfs__k_features'] == 3
def __param_search(self, clasif, train_data):
"""
:param clasif: clasificador para realizar la CV. String
:param train_data: datos para el entrenamiento
:return: objeto GridSearchCV
Recompone los parámetros a utilizar y realiza una validación cruzada
"""
param_grid = dict()
if self.preprocess_data is False:
pipeline = self.pipelines[clasif]
self.parameters[clasif].update(parameters.vect_params)
param_grid = self.parameters[clasif]
else:
pipeline = self.pipelines_train[clasif]
for k in self.parameters[clasif].keys():
param_grid[k[4:]] = self.parameters[clasif][k]
print(param_grid)
print(pipeline)
print("\n#Searching parameters for ", clasif) if self.v >= 1 else None
print("Parametros: ", param_grid) if self.v >= 2 else None
print("train_data: ", train_data) if self.v >= 3 else None
print("Longitudes: %d %d" % (len(train_data[0]), len(train_data[1]))) if self.v >= 2 else None
if self.bin_flag:
try:
grid_search = GridSearchCV(pipeline, param_grid, verbose=self.v, scoring='roc_auc', refit=True, cv=3)
except Exception as e:
os.system('cat %s >> archivo.txt' % e)
else:
grid_search = GridSearchCV(pipeline, param_grid, verbose=self.v, scoring='accuracy', refit=True, cv=3)
grid_search.fit(train_data[0], train_data[1])
return grid_search
def svr_linear(X,Y,x,y):
reg = GridSearchCV(SVR(kernel='linear'), cv=10,param_grid={"C":[1e0, 1e1, 1e2, 1e3], "degree":[1,2,3,4]})
reg.fit(X, Y)
y_predict = reg.predict(x)
rmse = RMSE(y=y, y_predict=y_predict)
print "rmse: ", str(rmse)
return rmse, y_predict
def PipeFeauture(Xtrain, Ytrain):
pipeline = Pipeline([
('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', SGDClassifier()),
])
# uncommenting more parameters will give better exploring power but will
# increase processing time in a combinatorial way
parameters = {
'vect__max_df': (0.5, 0.75, 1.0),
#'vect__max_features': (None, 5000, 10000, 50000),
'vect__ngram_range': ((1, 1), (1, 2)), # unigrams or bigrams
#'tfidf__use_idf': (True, False),
#'tfidf__norm': ('l1', 'l2'),
'clf__alpha': (0.00001, 0.000001),
'clf__penalty': ('l2', 'elasticnet'),
#'clf__n_iter': (10, 50, 80),
}
grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1, verbose=1)
print("Performing grid search...")
print("pipeline:", [name for name, _ in pipeline.steps])
print("parameters:")
pprint(parameters)
t0 = time()
grid_search.fit(Xtrain, Ytrain)
print("done in %0.3fs" % (time() - t0))
print()
print("Best score: %0.3f" % grid_search.best_score_)
print("Best parameters set:")
best_parameters = grid_search.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print("\t%s: %r" % (param_name, best_parameters[param_name]))
def test_count_vectorizer_pipeline_grid_selection():
# raw documents
data = JUNK_FOOD_DOCS + NOTJUNK_FOOD_DOCS
# label junk food as -1, the others as +1
target = [-1] * len(JUNK_FOOD_DOCS) + [1] * len(NOTJUNK_FOOD_DOCS)
# split the dataset for model development and final evaluation
train_data, test_data, target_train, target_test = train_test_split(
data, target, test_size=.2, random_state=0)
pipeline = Pipeline([('vect', CountVectorizer()),
('svc', LinearSVC())])
parameters = {
'vect__ngram_range': [(1, 1), (1, 2)],
'svc__loss': ('hinge', 'squared_hinge')
}
# find the best parameters for both the feature extraction and the
# classifier
grid_search = GridSearchCV(pipeline, parameters, n_jobs=1)
# Check that the best model found by grid search is 100% correct on the
# held out evaluation set.
pred = grid_search.fit(train_data, target_train).predict(test_data)
assert_array_equal(pred, target_test)
# on this toy dataset bigram representation which is used in the last of
# the grid_search is considered the best estimator since they all converge
# to 100% accuracy models
assert_equal(grid_search.best_score_, 1.0)
best_vectorizer = grid_search.best_estimator_.named_steps['vect']
assert_equal(best_vectorizer.ngram_range, (1, 1))
def logistic_regression(self):
C_array = [2**i for i in range(-10, 10)]
tuned_parameters = {'C' : C_array}
logi_reg = LogisticRegression()
clf = GridSearchCV(logi_reg, tuned_parameters, cv=5, scoring = "recall")# make_scorer(my_scorer))
clf.fit(self.train_data_x, self.train_labels_y)
self.models.append(clf)
def kernel_ridge_linear(X,Y,x,y):
reg = GridSearchCV(KernelRidge(kernel='linear'), cv=10,param_grid={"alpha": [1e0,0.1,1e-2,1e-3],"degree":[1,2,3,4] })
reg.fit(X, Y)
y_predict = reg.predict(x)
rmse = RMSE(y=y, y_predict=y_predict)
print "rmse: ", str(rmse)
return y_predict
def test_grid_search_correct_score_results():
# test that correct scores are used
n_splits = 3
clf = LinearSVC(random_state=0)
X, y = make_blobs(random_state=0, centers=2)
Cs = [.1, 1, 10]
for score in ['f1', 'roc_auc']:
grid_search = GridSearchCV(clf, {'C': Cs}, scoring=score, cv=n_splits)
results = grid_search.fit(X, y).cv_results_
# Test scorer names
result_keys = list(results.keys())
expected_keys = (("mean_test_score", "rank_test_score") +
tuple("split%d_test_score" % cv_i
for cv_i in range(n_splits)))
assert_true(all(in1d(expected_keys, result_keys)))
cv = StratifiedKFold(n_splits=n_splits)
n_splits = grid_search.n_splits_
for candidate_i, C in enumerate(Cs):
clf.set_params(C=C)
cv_scores = np.array(
list(grid_search.cv_results_['split%d_test_score'
% s][candidate_i]
for s in range(n_splits)))
for i, (train, test) in enumerate(cv.split(X, y)):
clf.fit(X[train], y[train])
if score == "f1":
correct_score = f1_score(y[test], clf.predict(X[test]))
elif score == "roc_auc":
dec = clf.decision_function(X[test])
correct_score = roc_auc_score(y[test], dec)
assert_almost_equal(correct_score, cv_scores[i])
accuracies.mean()
accuracies.std()
# Applying Grid Search to find the best model and the best parameters
from sklearn.model_selection import GridSearchCV
parameters = [{
'C': [1, 2, 3, 4],
'kernel': ['linear']
}, {
'C': [1, 2, 3, 4],
'kernel': ['rbf'],
'gamma': [0.67, 0.68, 0.69, 0.675, 0.685]
}]
grid_search = GridSearchCV(estimator=classifier,
param_grid=parameters,
scoring='accuracy',
cv=10,
n_jobs=-1)
grid_search = grid_search.fit(X_train, y_train)
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(
np.arange(start=X_set[:, 0].min() - 1,
stop=X_set[:, 0].max() + 1,
step=0.01),
np.arange(start=X_set[:, 1].min() - 1,
stop=X_set[:, 1].max() + 1,
def get_grid_result(self, param_grid):
model = KerasClassifier(build_fn=self.build_fn, epochs=self.epochs, batch_size=self.batch_size, verbose=0)
clf = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=self.split)
grid_result = clf.fit(self.train_x, self.train_y)
return grid_result
def A2_SVM_ParameterTuning(x_train, y_train):
param_grid = {'kernel': ('linear', 'poly', 'rbf'), 'C': [1, 10]}
grid = GridSearchCV(SVC(), param_grid=param_grid, cv=4) # GridSearchCV
grid.fit(x_train, y_train)
return grid.best_params_
kfold = StratifiedKFold(n_splits=10)
rf_param_grid = {
"max_depth": [None],
"max_features": [1, 3, 6],
"min_samples_split": [2, 3, 10],
"min_samples_leaf": [1, 3, 10],
"bootstrap": [False],
"n_estimators": [50, 100, 300, 500],
"criterion": ["gini", "entropy"]
}
from sklearn.model_selection import GridSearchCV
gsRFC = GridSearchCV(classifier,
param_grid=rf_param_grid,
cv=kfold,
scoring="accuracy",
n_jobs=-1,
verbose=1)
gsRFC.fit(X_train, y_train)
RFC_best = gsRFC.best_estimator_
gsRFC.best_score_
# Generate competition submission
X_comp, y_none = pre_process(comp_test_dataset)
comp_test_pred = gsRFC.predict(X_comp)
comp_output = pd.DataFrame(data=np.append(comp_test_dataset[["PassengerId"
# Model
estimator = RandomForestRegressor(n_estimators=250, criterion='mse',
n_jobs=15, verbose=1, random_state=0)
pipeline = Pipeline([
('imputation', make_union(SimpleImputer(strategy="median"),
MissingIndicator(error_on_new=False))),
('estimator', estimator)])
cv = ShuffleSplit(n_splits=100, test_size=0.1, random_state=0)
param_grid = {'estimator__max_depth': [5, 10, 20, 40, None],
'estimator__max_features': [1, 5, 'log2', 'sqrt', 'auto', None]}
grid_search = GridSearchCV(pipeline, param_grid=param_grid,
cv=5, verbose=2, n_jobs=15)
metrics = []
def predict_collect_save(data_pred, data_collect, y_true, test_index,
split, save_type):
scores = {}
pred_ = grid_search.predict(data_pred)
y_true_ = y_true.iloc[test_index]
predictions = pd.DataFrame(pred_, columns=['predicted'],
index=y_true_.index)
predictions['true'] = y_true_
predictions['test_indices'] = pd.DataFrame(test_index,
columns=['test indices'],
index=y_true_.index)
# We create a instance of model.
Estimator_DTree = DecisionTreeClassifier()
# Now, we are going to use a grid search cross-validation to explore combinations of parameters.
param_grid = {
'criterion': ['gini'],
'max_features': ['auto'],
'splitter': ['random', 'best'],
'min_samples_split': [25, 30, 35, 40, 45],
'max_depth': range(4, 6),
'random_state': [0]
}
Grid_DTree = GridSearchCV(Estimator_DTree,
param_grid,
cv=10,
verbose=2,
scoring='f1')
Grid_DTree.fit(X_train, Y_train)
# Once it has been fitted, we get several parameters.
#print("ParameterGrid: ",'\n',list(ParameterGrid(param_grid)),'\n')
print("Best estimator: ", Grid_DTree.best_estimator_, '\n')
print("Best Score: ", round(Grid_DTree.best_score_, 2))
print("Best Parameters ", Grid_DTree.best_params_)
# Now, we came back fit it Best_Grid_estimator with.
Best_Grid_estimator_DTree = Grid_DTree.best_estimator_
return normalized_gini
gini_scorer = make_scorer(normalized_gini, greater_is_better=True)
cv=StratifiedShuffleSplit(n_splits=5, test_size=0.2)
gsc = GridSearchCV(
estimator=rf,
param_grid={
#'class_weight': [{0: 1, 1: x} for x in range(300, 701, 100)] + ['balanced'],
#'min_samples_leaf': range(5,51,5),
#'min_samples_split': range(5,56,10),
#'n_estimators': range(200, 601, 200),
#'criterion': ('gini', 'entropy')
#'max_features': ('auto', 'sqrt'),
#'max_features': range(3, 9),
#'max_depth': range(3, 7),
},
#scoring='neg_log_loss',
scoring='roc_auc',
#scoring='f1',
#scoring=gini_scorer,
cv=cv,
verbose=2
)
rsc = RandomizedSearchCV(
estimator=rf,
param_distributions={
'n_estimators': randint(250, 2500),
#'class_weight': [{0: 1, 1: x} for x in range(15, 51, 5)],
environ[
"PYTHONWARNINGS"] = "ignore" # Also affect subprocesses (n_jobs > 1)
# Linear SVM
print("\rLinear SVM ", end='')
parameters = {'C': [0.01, 0.1, 1, 10, 100]}
# svc = svm.LinearSVC(class_weight=args.class_weight, random_state=seed)
svc = svm.SVC(kernel='linear',
class_weight=args.class_weight,
random_state=args.seed,
probability=True,
max_iter=max_iters)
clf = GridSearchCV(svc,
parameters,
cv=sss,
n_jobs=-1,
scoring=scoring,
refit='roc_auc',
return_train_score=True)
try:
clf.fit(data, labels)
except Exception as e:
if hasattr(e, 'message'):
print(e.message)
else:
print(e)
save_results(args.savefile, 'a', 'Linear SVM', clf)
# RBF SVM
print("\rRBF SVM ", end='')
from sklearn.preprocessing import MinMaxScaler
Scaler = MinMaxScaler()
X_train_scaler = Scaler.fit_transform(X_train)
X_test_scaler = Scaler.transform(X_test)
# In[ ]:
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report,roc_auc_score,precision_score,recall_score
LoR = LogisticRegression(random_state=0)
c_values = {'C':[1,15,10,100,150,250]}
grdClf = GridSearchCV(LoR,param_grid=c_values,scoring='precision')
grdClf.fit(X_train_scaler,y_train)
y_decs = grdClf.decision_function(X_test_scaler)
(grdClf.best_params_,grdClf.best_score_,roc_auc_score(y_test,y_decs))
# Performing Cross validation to evaluate the model
# In[ ]:
from sklearn.model_selection import cross_val_score
cross_val_score(DTClf,X_train,y_train,cv=5,scoring='precision')
# In[ ]:
print("Accuracy of logistic regression classifier: ", logreg.score(rescaledX_test,y_test))
confusion_matrix(y_test,y_pred)
# From our confusion matrix we can see that accuracy is pretty low.
# In[35]:
from sklearn.model_selection import GridSearchCV
tol = [0.01, 0.001, 0.0001]
max_iter = [100, 150, 200]
param_grid = {'tol':tol, 'max_iter': max_iter}
# In[38]:
grid_model = GridSearchCV(logreg,param_grid,cv = 5)
rescaledX = scaler.fit_transform(X)
grid_model_result = grid_model.fit(rescaledX,y)
best_score, best_params = grid_model_result.best_score_, grid_model_result.best_params_
print(best_score, best_params)
# Best Score : 0.186
from sklearn.pipeline import Pipeline
# TODO: 1.生成数据集并进行数据划分
X, y = make_blobs(n_samples=200, centers=2, cluster_std=5, random_state=16)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=16)
# TODO: 2.创建管道模型，并创建预处理模块scaler和神经网络模块mlp
params = {
'mlp__hidden_layer_sizes': [[50], [100], [100, 100]],
'mlp__alpha': [0.0001, 0.001, 0.01, 0.1]
}
pipeline = Pipeline(steps=[('scaler', StandardScaler()),
('mlp', MLPClassifier(max_iter=1600,
random_state=16))],
verbose=0)
# TODO: 3.创建网格搜索模型，并输出预测结果
grid = GridSearchCV(pipeline,
param_grid=params,
cv=5,
iid=False,
n_jobs=8,
verbose=1)
grid.fit(X_train, y_train)
print('交叉验证评分:{:.2f}'.format(grid.best_score_))
print('模型最优参数：{}'.format(grid.best_params_))
print('测试集得分：{}'.format(grid.score(X_test, y_test)))
print(pipeline.steps)
def model_selection(X, y, rep=10, random_state=42):
""" Uses grid search and cross validation to choose the best clf for the task (X,y)"""
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=random_state)
# models = [
# ## ("XGB",XGBClassifier(seed=random_state,objective = "multi:softmax")),
# ("NaiveBayes",GaussianNB()),
# ("RF",RandomForestClassifier(random_state=random_state)),
# ("AdaBoost",AdaBoostClassifier(random_state=random_state)),
# ("LR",LogisticRegression(random_state=random_state)),
# ## ("linSVC",LinearSVC(multi_class="ovr")),
# ("SVC",SVC(random_state=random_state,probability=True)),
# ("MLP",MLPClassifier(random_state=random_state))
# # ("KNN",KNeighborsClassifier())
# ]
# hyperparameters = [
# ## [("max_depth",[15,20]),("n_estimators",[100,200])],
# [],
# [("max_depth",[5,6,10])],
# [("n_estimators",[10,50,100])],
# [("C",[1.0,0.95,0.9])],
# ## [],
# [],
# [("hidden_layer_sizes",[(200,),(150,),(100,100),(300,)])]
# # [("n_neighbors",[5,6,7,10])]
# ]
best_est = None
best_score = 0.0
results_summary = []
all_models = []
for model, hyperp_setting in zip(models_clfs, hyperparameters_clfs):
print("Fitting " + model[0])
pipeline = Pipeline([model])
# pipeline = Pipeline([("scaling",StandardScaler()),model])
param_grid = {}
for param in hyperp_setting:
param_grid[model[0] + "__" + param[0]] = param[1]
grid_search = GridSearchCV(pipeline,
param_grid=param_grid,
verbose=True,
scoring="f1_weighted",
cv=3,
n_jobs=5)
grid_search.fit(X_train, y_train)
clf = grid_search.best_estimator_
scores = []
np.random.seed(random_state)
for i in range(0, rep):
rows = np.random.randint(2, size=len(X_train)).astype('bool')
clf.fit(X_train[rows], y_train[rows])
preds = clf.predict(X_test)
scores.append(f1_score(y_test, preds, average='weighted'))
results_summary.append([model, scores])
print(results_summary[-1])
avg_score = pd.DataFrame(scores).mean()[0]
if (avg_score > best_score):
best_score = avg_score
best_est = clf
clf.fit(X, y)
all_models.append([model[0], clf])
y_pred = best_est.predict(X_test)
rocs = []
preds_score = best_est.predict_proba(X_test)
for i in range(0, len(best_est.classes_)):
correct_class = best_est.classes_[i]
fpr, tpr, _ = roc_curve(y_test.as_matrix(),
[p[i] for p in preds_score],
pos_label=correct_class)
roc_df = pd.DataFrame(tpr, columns=["tpr"]).join(
pd.DataFrame(fpr, columns=["fpr"])).join(
pd.DataFrame([correct_class] * len(tpr), columns=["class"]))
rocs.append(roc_df)
rocs_df = pd.concat(rocs)
#using whole data after cv
best_est.fit(X, y)
return best_est, best_score, confusion_matrix(
y_test, y_pred), rocs_df, results_summary, all_models
# model details
model_name = f'{args.model}_t{args.topic_num:02d}_l{args.max_seq_len}_d{args.drop_out}'
model_group = models_info[args.model]['group']
params = models_info[args.model]['params']
# model training
print(f'{model_name} training...')
if model_group == 'sklearn':
# build models
model = build_SKM(model_type=args.model,
max_features=20000,
selectK=10000)
# grid search
clf = GridSearchCV(model, params, cv=3, n_jobs=-1)
clf.fit(X_train, y_train)
# train with best params
model.set_params(**clf.best_params_)
model.fit(X_train, y_train)
# test and save
save_file(model, 'models', model_name)
results = model_evaluation(X_test, y_test, model=model)
if model_group == 'keras':
# preprocessing
X_train, word_index, doc2seq = tokenizer_transform(
X_train,
# #'model__beta_2' : [1e-7, 1e-8, 1e-9]
#
# }
# Two options:
# 1) Grid search to find best model
# 2) Train best model and save to disk
# Option 1) Grid search to find best model
if args.type == "gridsearch":
print("Start Grid search...")
X_train, X_test, y_train, y_test = train_test_split(
V, labels.values.ravel(), test_size=0.3, random_state=0)
grid = GridSearchCV(pipeline, parameters, cv=10, n_jobs=-1, verbose=1)
#grid = grid.fit(V, labels.values.ravel())
grid = grid.fit(X_train, y_train)
y_pred = grid.best_estimator_.predict(X_test)
print("\nBest: %f using %s" % (grid.best_score_, grid.best_params_))
print("F1-Score:", f1_score(y_test, y_pred))
print("Precision: ", precision_score(y_test, y_pred))
print("Recall: ", recall_score(y_test, y_pred))
print("Accuracy: ", accuracy_score(y_test, y_pred))
print("roc auc: ", roc_auc_score(y_test, y_pred))
print("Performance overall: ")
print(classification_report(y_test, y_pred))
random_state=42)
rnd_search_cv.fit(train_prepared, price_labels)
#%%
rnd_search_cv.best_estimator_
#%%
#Random Forest Regressor
from sklearn.linear_model import Ridge
ridge_reg = Ridge(alpha=1, solver="cholesky", random_state=42)
ridge_reg.fit(train_prepared, price_labels)
print(ridge_reg.predict(test_prepared))
#%%
#Random Forest
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import GridSearchCV
parameters = {'n_estimators': (1000, 10, 100), 'max_features': [15, 5, 10]}
forest = RandomForestRegressor(random_state=0)
gsearch = GridSearchCV(forest, parameters, cv=5)
gsearch.fit(X_train, y_train)
#%%
gsearch.best_estimator_
gsearch.best_score_
#%%
forest_rmse_scores = np.sqrt(-forest_scores)
display_scores("Random Forest Regression", forest_rmse_scores)
#SupportVectorRegressor Validation
sv_scores = cross_val_score(sv_reg,
X_train,
Y_train,
scoring="neg_mean_squared_error",
cv=10)
sv_rmse_scores = np.sqrt(-sv_scores)
display_scores("Support Vector Regression", sv_rmse_scores)
#8.Fine-Tune RandomForestRegressor Model using Grid Search
param_grid = [{'n_estimators': [10, 20, 30, 40], 'max_features': [4, 8]}]
grid_search = GridSearchCV(forest_reg,
param_grid,
cv=10,
scoring='neg_mean_squared_error')
grid_search.fit(X_train, Y_train)
cvres = grid_search.cv_results_
for mean_score, params in zip(cvres["mean_test_score"], cvres["params"]):
print(np.sqrt(-mean_score), params)
print("Best Parameter: ")
print(grid_search.best_params_)
final_model = grid_search.best_estimator_
final_predictions = final_model.predict(X_test)
final_mse = mean_squared_error(Y_test, final_predictions)
final_rmse = np.sqrt(final_mse)
print("Final Prediction: ")
print(final_rmse)
class GradientBoost:
def __init__(self):
"""
Don't pass in anything
"""
self.m = GradientBoostingClassifier()
def log_loss_score(self, y_true, y_pred):
"""
input:
y_true, y_pred: 1d arrays of size n
output:
float: log loss score. It's a negative number, closer to zero is better.
"""
return -log_loss(y_true, y_pred)
def _change_data(self, old_df):
#return jconvert(a_convert(old_df))
"""
take dataframe, output dataframe
"""
return max_data_pipeline(jconvert(a_convert(do_it(old_df))))
def fit(self, data):
"""
data is a dataframe
no output
"""
self.data = self._change_data(data)
self.X = self.data.drop(['fraud'], axis=1).values
self.y = self.data['fraud'].values
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
self.X, self.y)
self.m.fit(self.X_train, self.y_train)
def optimize(self, param_grid, n_jobs=-1, cv=5):
"""
run grid search to change model
"""
self.grid = GridSearchCV(self.m,
param_grid=param_grid,
cv=cv,
n_jobs=n_jobs,
scoring='neg_log_loss')
self.grid.fit(self.X_train, self.y_train)
self.m = self.grid.best_estimator_
def score(self):
return self.log_loss_score(self.y_test,
self.m.predict_proba(self.X_test))
def predict(self, df):
X = self._change_data(df).values
return self.m.predict(X)
def predict_proba(self, df):
converted = self._change_data(df)
default_col = pd.Series([0] * len(converted))
for col in self.data.columns:
if col not in converted.columns and col != 'fraud':
converted[col] = default_col
return self.m.predict_proba(converted.values)
def cross_validate(featnames,
tasknames,
cvs,
classifiers,
gps,
logger,
n_jobs,
npy_suffix='',
mid_layer=4):
'''featnames: list of string, ['mine', 'mfcc']
- tasknames = list of stringm ['ballroom_extended', 'gtzan_genre', 'gtzan_speechmusic',
'emoMusic', 'jamendo_vc', 'urbansound']
- cvs: list of cv, 10 for rest, split arrays for urbansound and jamendo_vd
- classifier: list of classifier class, e.g [KNeighborsClassifier, SVC]
- gps: list of gp, e.g. [{"n_neighbors":[1, 2, 8, 12, 16]}, {"C":[0.1, 8.0], "kernel":['linear', 'rbf']}]
- mid_layer: scalar, or list of scalar .
'''
np.random.seed(1209)
if not isinstance(mid_layer, list):
mid_layer = [mid_layer]
logger.info('')
logger.info('--- Cross-validation started for {} ---'.format(''.join(
[str(i) for i in mid_layer])))
for featname in featnames:
logger.info(' * feat_name: {} ---'.format(featname))
for classifier, gp in zip(classifiers, gps):
clname = classifier.__name__
logger.info(' - classifier: {} ---'.format(clname))
for taskname, cv in zip(tasknames, cvs):
logger.info(' . task: {} ---'.format(taskname))
model_filename = 'clf_{}_{}_{}.cP'.format(
featname, taskname, clname)
x, y = load_xy_many(taskname,
featname,
npy_suffix,
logger,
mid_layer=mid_layer)
estimators = [('stdd', OptionalStandardScaler()),
('clf', classifier())]
pipe = Pipeline(estimators)
if isinstance(gp, dict): # k-nn or svm with single kernel
params = {'stdd__on': [True, False]}
params.update({
'clf__' + key: value
for (key, value) in gp.iteritems()
})
elif isinstance(
gp,
list): # svm: grid param can be a list of dictionaries
params = []
for dct in gp: # should be dict of list for e.g. svm
sub_params = {'stdd__on': [True, False]}
sub_params.update({
'clf__' + key: value
for (key, value) in dct.iteritems()
})
params.append(sub_params)
clf = GridSearchCV(pipe,
params,
cv=cv,
n_jobs=n_jobs,
pre_dispatch='8*n_jobs').fit(x, y)
logger.info(' . best score {}'.format(clf.best_score_))
logger.info(clf.best_params_)
print('best score of {}, {}, {}: {}'.format(
featname, taskname, clname, clf.best_score_))
print(clf.best_params_)
cP.dump(clf, open(os.path.join(PATH_CLS, model_filename), 'w'))
featname_midlayer = '{}_{}'.format(
featname, ''.join([str(i) for i in mid_layer]))
save_result(featname_midlayer, taskname, clname,
clf.best_score_)
import pandas as pd
(X, y), _ = load_preproccessed_dataset(test_split=0.0, include_grades=True)
hyperparams = {
'n_estimators': [
100,
500,
],
'max_depth': [
3,
None,
],
'min_samples_leaf': [
1,
0.05,
],
}
clf = GridSearchCV(estimator=RandomForestClassifier(),
param_grid=hyperparams,
cv=10)
clf.fit(X, y)
cv_results = pd.DataFrame(clf.cv_results_)
print(cv_results[[
*(f'param_{p}' for p in hyperparams.keys()), 'mean_fit_time',
'mean_test_score', 'std_test_score'
]])
# In[60]:
y_pred_c
# # GridSearchCV
# In[63]:
parameters = [{'gamma': [0.001, 0.005, 0.01, 0.02, 0.05, 0.1],
'C': [0.1, 0.2, 0.25, 0.5, 1, 1.5, 2]}]
#'nu': [0.75, 0.8, 0.85, 0.9, 0.95, 0.97]}]
reg1 = GridSearchCV(SVR(kernel='rbf', tol=0.01), parameters, cv=5, scoring='neg_mean_absolute_error')
reg1.fit(x_train, y_train.flatten())
y_pred1 = reg1.predict(x_train)
print("Best CV score: {:.4f}".format(reg1.best_score_))
print(reg1.best_params_)
#print(y_pred1)
# In[ ]:
# In[ ]:
# open a file, where you ant to store the data
file = open('regression_model.pkl', 'wb')
# dump information to that file
pickle.dump(reg, file)
########################## Ridge Regression #######################
from sklearn.linear_model import Ridge
from sklearn.model_selection import GridSearchCV
ridge = Ridge()
parameters = {
'alpha': [1e-15, 1e-10, 1e-8, 1e-3, 1e-2, 1, 5, 10, 20, 30, 35, 40]
}
ridge_regressor = GridSearchCV(ridge,
parameters,
scoring='neg_mean_squared_error',
cv=5)
ridge_regressor.fit(X, y)
print(ridge_regressor.best_params_)
print(ridge_regressor.best_score_)
########################## Lasso Regression #######################
from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
lasso = Lasso()
parameters = {
'alpha': [1e-15, 1e-10, 1e-8, 1e-3, 1e-2, 1, 5, 10, 20, 30, 35, 40]
}
lasso_regressor = GridSearchCV(lasso,
# random_state=100, topic_word_prior=None,
# total_samples=1000000.0, verbose=0)
# Log Likelyhood: Higher the better
print("Log Likelihood: ", lda_model.score(data_vectorized))
# Perplexity: Lower the better. Perplexity = exp(-1. * log-likelihood per word)
print("Perplexity: ", lda_model.perplexity(data_vectorized))
# See model parameters
pprint(lda_model.get_params())
# Define Search Param
search_params = {'n_components': [10, 15, 20, 25, 30], 'learning_decay': [.5, .7, .9]}
# Init the Model
lda = LatentDirichletAllocation(max_iter=5, learning_method='online', learning_offset=50., random_state=0)
# Init Grid Search Class
model = GridSearchCV(lda, param_grid=search_params)
# Do the Grid Search
model.fit(data_vectorized)
GridSearchCV(cv=None, error_score='raise',
estimator=LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,
evaluate_every=-1, learning_decay=0.7, learning_method=None,
learning_offset=10.0, max_doc_update_iter=100, max_iter=10,
mean_change_tol=0.001, n_components=10, n_jobs=1,
n_topics=None, perp_tol=0.1, random_state=None,
topic_word_prior=None, total_samples=1000000.0, verbose=0),
fit_params=None, iid=True, n_jobs=1,
param_grid={'n_topics': [10, 15, 20, 25, 30], 'learning_decay': [0.5, 0.7, 0.9]},
pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
scoring=None, verbose=0)
def findClassifierParameters(clumpsImg,
classesIntCol,
variables,
preProcessor=None,
gridSearch=GridSearchCV(RandomForestClassifier(),
{})):
"""
Find the optimal parameters for a classifier using a grid search and return a classifier instance with those optimal parameters.
:param clumpsImg: is the clumps image on which the classification is to be performed
:param classesIntCol: is the column with the training data as int values
:param variables: is an array of column names which are to be used for the classification
:param preProcessor: is a scikit-learn processors such as sklearn.preprocessing.MaxAbsScaler() which can rescale the input variables independently as read in (Define: None; i.e., not in use).
:param gridSearch: is an instance of GridSearchCV parameterised with a classifier and parameters to be searched.
:return: Instance of the classifier with optimal parameters defined.
Example::
from rsgislib.classification import classratutils
from sklearn.svm import SVC
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import MaxAbsScaler
clumpsImg = "./LS8_20150621_lat10lon652_r67p233_clumps.kea"
classesIntCol = 'ClassInt'
classParameters = {'kernel':['linear', 'rbf', 'poly', 'sigmoid'], 'C':[1, 2, 3, 4, 5, 10, 100, 400, 500, 1e3, 5e3, 1e4, 5e4, 1e5], 'gamma':[0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1, 'auto'], 'degree':[2, 3, 4, 5, 6, 7, 8], 'class_weight':['', 'balanced'], 'decision_function_shape':['ovo', 'ovr', None]}
variables = ['BlueRefl', 'GreenRefl', 'RedRefl', 'NIRRefl', 'SWIR1Refl', 'SWIR2Refl']
gSearch = GridSearchCV(SVC(), classParameters)
classifier = classratutils.findClassifierParameters(clumpsImg, classesIntCol, variables, preProcessor=MaxAbsScaler(), gridSearch=gSearch)
"""
# Check gdal is available
if not haveGDALPy:
raise Exception(
"The GDAL python bindings required for this function could not be imported\n\t"
+ gdalErr)
# Check numpy is available
if not haveNumpy:
raise Exception(
"The numpy module is required for this function could not be imported\n\t"
+ numErr)
# Check rios rat is available
if not haveRIOSRat:
raise Exception(
"The RIOS rat tools are required for this function could not be imported\n\t"
+ riosRatErr)
# Check scikit-learn pre-processing is available
if not haveSKLearnPreProcess:
raise Exception(
"The scikit-learn pre-processing tools are required for this function could not be imported\n\t"
+ sklearnPreProcessErr)
# Check scikit-learn Grid Search is available
if not haveSKLearnGS:
raise Exception(
"The scikit-learn grid search tools are required for this function could not be imported\n\t"
+ sklearnGSErr)
ratDataset = gdal.Open(clumpsImg, gdal.GA_Update)
numpyVars = []
for var in variables:
print("Reading " + var)
tmpArr = rat.readColumn(ratDataset, var)
if not preProcessor is None:
tmpArr = tmpArr.reshape(-1, 1)
tmpArr = preProcessor.fit_transform(tmpArr)
tmpArr = tmpArr.reshape(-1)
numpyVars.append(tmpArr)
# Read in training classes
classesInt = rat.readColumn(ratDataset, classesIntCol)
xData = numpy.array(numpyVars)
xData = xData.transpose()
xData = numpy.where(numpy.isfinite(xData), xData, 0)
print("Input data size: {} x {}".format(xData.shape[0], xData.shape[1]))
trainingData = xData[numpy.isfinite(xData).all(axis=1)]
classesInt = classesInt[numpy.isfinite(xData).all(axis=1)]
trainingData = trainingData[classesInt > 0]
classesInt = classesInt[classesInt > 0]
print("Training data size: {} x {}".format(trainingData.shape[0],
trainingData.shape[1]))
print("Training data IDs size: {}".format(classesInt.shape[0]))
classIDs = numpy.unique(classesInt)
print(classIDs)
for id in classIDs:
print("Class {} has {} samples.".format(
id, classesInt[classesInt == id].shape[0]))
gridSearch.fit(trainingData, classesInt)
if not gridSearch.refit:
raise Exception("Grid Search did no find a fit therefore failed...")
print("Best score was {} and has parameters {}.".format(
gridSearch.best_score_, gridSearch.best_params_))
return gridSearch.best_estimator_
# X = sc.fit_transform(X)
# X_test = sc.transform(X_test)
# =============================================================================
################################################################################
# logistic regression
################################################################################
logistic = linear_model.LogisticRegression()
# Create regularization penalty space
penalty = ['l1', 'l2']
# Create regularization hyperparameter space
C = np.logspace(0, 4, 10)
# Create hyperparameter options
hyperparameters = dict(C=C, penalty=penalty)
# Create grid search using 5-fold cross validation
clf = GridSearchCV(logistic, hyperparameters, cv=5, verbose=0, n_jobs=-1)
# Fit grid search
best_model = clf.fit(X, y)
# View best hyperparameters
print('Best Penalty:', best_model.best_estimator_.get_params()['penalty'])
print('Best C:', best_model.best_estimator_.get_params()['C'])
# prediction on test data using the best model
# =============================================================================
# y_pred = best_model.predict(X_test)
# =============================================================================
################################################################################
# random forest regression
################################################################################
# =============================================================================
# from sklearn.ensemble import RandomForestRegressor
'knn__weights': ['uniform', 'distance']
}
# --------------------------------
pipe_ext = Pipeline([
('ext', ExtraTreesClassifier())
])
pipe_ext_params = {
'ext__n_estimators': [100, 150, 200],
'ext__max_depth': [None, 1, 2, 3, 4],
'ext__min_samples_split': [2, 3, 4],
'ext__min_samples_leaf': [1, 2, 3, 4]
}
gs_lr = GridSearchCV(pipe_lr, pipe_lr_params, cv=3, verbose=0, n_jobs = -1)
gs_rf= GridSearchCV(pipe_rf, pipe_rf_params, cv=3, verbose=0, n_jobs = -1)
gs_gbc = GridSearchCV(pipe_gbc, pipe_gbc_params, cv =3, verbose=0, n_jobs = -1)
gs_knn = GridSearchCV(pipe_knn, pipe_knn_params, cv =3, verbose=0, n_jobs = -1)
gs_ext = GridSearchCV(pipe_ext, pipe_ext_params, cv =3, verbose=0, n_jobs = -1)
gs_lr.fit(X_t_train_sc, y_t_train)
gs_rf.fit(X_t_train_sc, y_t_train)
gs_gbc.fit(X_t_train_sc, y_t_train)
gs_knn.fit(X_t_train_sc, y_t_train)
gs_ext.fit(X_t_train_sc, y_t_train)
df = pd.DataFrame({'default.payment.next.month': next_month.index,'values': next_month.values})
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.figure(figsize = (6,6))
plt.title('信用卡违约率客户\n (违约：1，守约：0)')
sns.set_color_codes("pastel")
sns.barplot(x = 'default.payment.next.month', y="values", data=df)
locs, labels = plt.xticks()
plt.show()
# 特征选择，去掉ID字段、最后一个结果字段即可
data.drop(['ID'], inplace=True, axis =1) #ID这个字段没有用
target = data['default.payment.next.month'].values
columns = data.columns.tolist()
columns.remove('default.payment.next.month')
features = data[columns].values
# 30%作为测试集，其余作为训练集
train_x, test_x, train_y, test_y = train_test_split(features, target, test_size=0.30, stratify = target, random_state = 1)
#分类器
ada=AdaBoostClassifier( random_state=1)
#需要调整的参数
parameters={'n_estimators':[10,50,100]}
# 使用 GridSearchCV 进行参数调优
clf=GridSearchCV(estimator=ada,param_grid=parameters,scoring = 'accuracy')
clf.fit(train_x,train_y)
print("GridSearch最优参数：", clf.best_params_)
print("GridSearch最优分数： %0.4lf" %clf.best_score_)
predict_y=clf.predict(test_x)
print("准确率 %0.4lf" %accuracy_score(test_y, predict_y))
('bow',
CountVectorizer(strip_accents='ascii',
stop_words='english',
lowercase=True)), # strings to token integer counts
('tfidf', TfidfTransformer()), # integer counts to weighted TF-IDF scores
('classifier',
MultinomialNB()), # train on TF-IDF vectors w/ Naive Bayes classifier
])
# this is where we define the values for GridSearchCV to iterate over
parameters = {
'bow__ngram_range': [(1, 1), (1, 2)],
'tfidf__use_idf': (True, False),
'classifier__alpha': (1e-2, 1e-3),
}
# do 10-fold cross validation for each of the 6 possible combinations of the above params
grid = GridSearchCV(pipeline, cv=10, param_grid=parameters, verbose=1)
grid.fit(X_train, y_train)
# summarize results
print("\nBest Model: %f using %s" % (grid.best_score_, grid.best_params_))
print('\n')
means = grid.cv_results_['mean_test_score']
stds = grid.cv_results_['std_test_score']
params = grid.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("Mean: %f Stdev:(%f) with: %r" % (mean, stdev, param))
X_test = combi['tidy_tweet'][31962:]
# save best model to current working directory
joblib.dump(grid, "twitter_sentiment.pkl")
# load from file and predict using the best configs found in the CV step
model_NB = joblib.load("twitter_sentiment.pkl")
def get_predict_model():
parent = root + '/type_err_feature/'
types = os.listdir(parent)
for t in types:
print(t)
if not os.path.exists(parent + t + '/gdbt.model'):
#if True:
data = pd.read_csv(parent + t + '/err_feature.csv',
encoding='utf-8',
usecols=[
'result', 'YEAR_USE', 'dow', 'doy', 'month',
'hour', 'result_before_1', 'result_before_7'
])
n_estimators = range(40, 81, 10)
min_sample_split = range(20, 81, 5)
if data.shape[0] > 1000000:
n_estimators = range(250, 501, 50)
min_sample_split = range(120, 241, 30)
elif data.shape[0] > 500000:
n_estimators = range(100, 351, 50)
min_sample_split = range(60, 141, 20)
col = [
c for c in data.columns.tolist()
if c not in ['TIME', 'PAR_ROOM', 'NE_OBJ_ID', 'result']
]
trainX, testX, train_y, test_y = train_test_split(
data[col],
data['result'],
test_size=0.3,
random_state=80,
stratify=data['result'])
param_grid = {
'n_estimators': n_estimators,
'min_samples_split': min_sample_split
}
estimator = GradientBoostingClassifier(random_state=80,
max_depth=5,
learning_rate=0.005)
cv = StratifiedShuffleSplit(n_splits=3,
test_size=0.3,
random_state=80)
gbm = GridSearchCV(estimator=estimator,
param_grid=param_grid,
refit=True,
n_jobs=-1,
return_train_score=True,
scoring='roc_auc',
cv=cv)
# 训练和输出
if train_y.values.tolist().count(1) >= 3:
gbm.fit(trainX, train_y)
# 预测
y_pred = gbm.predict(testX)
y_predprob = gbm.predict_proba(testX)[:, 1]
print("型号 " + t + " roc_auc得分为：" + str(
metrics.roc_auc_score(
test_y, y_predprob, average='weighted')))
# 输出结果至csv
clf = gbm.best_estimator_
result = pd.DataFrame()
result["BEST_PARAMS"] = [str(gbm.best_params_)]
result["FEATURE_RANK"] = [str(clf.feature_importances_)]
result["TRAIN_ROC_AUC"] = [str(gbm.best_score_)]
result["ACCURACY"] = [
metrics.accuracy_score(test_y.values, y_pred)
]
result["ROC_AUC"] = [
metrics.roc_auc_score(test_y,
y_predprob,
average='weighted')
]
result['y_pred'] = [y_pred.tolist()]
result['y_true'] = [test_y.tolist()]
result.to_csv(parent + t + '/gdbt_result.csv',
header=True,
index=False,
encoding='utf-8')
joblib.dump(clf, parent + t + '/gdbt.model')
else:
print(t + "型号样本故障数据过少，无法训练模型")
X, y = load_data("Dataset")
F = extract_lbp_features(X)
# Apply grid and search for SCV classifier
# Create hyperparameter options
hyperparams = {
'C': [0.001, 0.005, 0.01, 0.5, 1, 5, 10, 50, 100, 500, 1000],
'gamma': [1, 0.1, 0.001, 0.0001],
'kernel': ['linear', 'rbf', 'poly'],
'degree': [1, 2, 3, 4]
}
grid_svc = GridSearchCV(SVC(), hyperparams, cv=5)
grid_svc.fit(F, y)
print('the best parameters for SVC classifier using GridSearchCV are ' +
str(grid_svc.best_params_))
searchcv_svc = RandomizedSearchCV(SVC(), hyperparams, n_iter=20, cv=5)
searchcv_svc.fit(F, y)
print('the best parameters for SVC classifier using RandomizedSearchCV are ' +
str(searchcv_svc.best_params_))
# Apply grid and search for logreg classifier
# Confusion Matrix for model 2
xgb_cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix :\n", xgb_cm)
print('-' * 50)
# Classification Report for model 2
xgb_cr = classification_report(y_test, y_pred)
print("Classification Report :\n", xgb_cr)
print('-' * 50)
print(
"*************Model3 (XGBoost Classifier using grid search)*************")
# Initialize Grid search model
xgb_clf = XGBClassifier(random_state=0)
clf_model = GridSearchCV(estimator=xgb_clf, param_grid=parameters)
# Fit the grid model
clf_model.fit(X_train, y_train)
# Prediction for the grid model
y_pred = clf_model.predict(X_test)
# Accuracy of the grid model
clf_score = accuracy_score(y_test, y_pred)
print("Accuracy score of model 3 :", round(clf_score, 2))
print('-' * 50)
# Confusion Matrix for the grid model
clf_cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix :\n", clf_cm)
def best_estimator(classifier,features_train,labels_train,features_test):
param_mapping={
'lor':{
'reduce_dim__n_components':list(range(1,10)),
'lor__C':[0.00000001,0.00001,1.0],
'lor__tol':[1e-3,1e-1],
'lor__penalty':['l1','l2'],
'lor__random_state':[42]
},
'svc':{
'svc__kernel':['rbf'],
'svc__C':[10000,100000,1000],
'svc__gamma':[0.001,0.0001,'auto'],
'svc__random_state':[68]},
'dtc':{
'dtc__criterion':['entropy','gini'],
'dtc__min_samples_split':[5,10,8,10,12],
'dtc__random_state':[68],
'dtc__min_samples_leaf':[4,6,8,10,12],
},
'knn':{
'reduce_dim__n_components':list(range(1,10)),
'knn__n_neighbors':[5,7,11],
'knn__algorithm':['ball_tree','kd_tree','brute','auto'],
'knn__leaf_size':[2,3,5,10,12]}
}
steps={
'svc':[(),('scale',StandardScaler()),('svc',SVC())],
'dtc':[('scale',StandardScaler()),('dtc',DecisionTreeClassifier())],
'knn':[('scale',StandardScaler()),('reduce_dim',PCA()),('knn',KNeighborsClassifier())],
'lor':[('scale',StandardScaler()),('reduce_dim',PCA()),('lor',LogisticRegression())]
}
pipe = Pipeline(steps[classifier])
tune_params=param_mapping[classifier]
sss = StratifiedShuffleSplit(n_splits=50, test_size=0.1, random_state=42)
grid_search = GridSearchCV(estimator=pipe,
param_grid=tune_params,
scoring='f1',
error_score=0,
cv=sss
)
grid_search.fit(features_train, labels_train)
predictions = grid_search.predict(features_test)
clf1 = grid_search.best_estimator_
clf1_parm=grid_search.best_params_
return clf1,clf1_parm
def plot_cross_val_selection():
iris = load_iris()
X_trainval, X_test, y_trainval, y_test = train_test_split(iris.data,
iris.target,
random_state=0)
param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],
'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}
grid_search = GridSearchCV(SVC(), param_grid, cv=5)
grid_search.fit(X_trainval, y_trainval)
results = pd.DataFrame(grid_search.cv_results_)[15:]
best = np.argmax(results.mean_test_score.values)
plt.figure(figsize=(10, 3))
plt.xlim(-1, len(results))
plt.ylim(0, 1.1)
for i, (_, row) in enumerate(results.iterrows()):
scores = row[['test_split%d_test_score' % i for i in range(5)]]
marker_cv, = plt.plot([i] * 5, scores, '^', c='gray', markersize=5,
alpha=.5)
marker_mean, = plt.plot(i, row.mean_test_score, 'v', c='none', alpha=1,
markersize=10, markeredgecolor='k')
if i == best:
marker_best, = plt.plot(i, row.mean_test_score, 'o', c='red',
fillstyle="none", alpha=1, markersize=20,
markeredgewidth=3)
plt.xticks(range(len(results)), [str(x).strip("{}").replace("'", "") for x
in grid_search.cv_results_['params']],
rotation=90)
plt.ylabel("Validation accuracy")
plt.xlabel("Parameter settings")
plt.legend([marker_cv, marker_mean, marker_best],
["cv accuracy", "mean accuracy", "best parameter setting"],
loc=(1.05, .4))
def main():
# 1 查看训练集和测试集的数据特征
train_data = pandas.read_csv('data/train.csv')
test_data = pandas.read_csv('data/test.csv')
print(train_data.info())
print(test_data.info())
# 2 人工选取预测有效的特征
selected_features = ['Pclass', 'Sex', 'Age', 'Embarked', 'SibSp', 'Parch', 'Fare']
x_train = train_data[selected_features]
x_test = test_data[selected_features]
y_train = train_data['Survived']
# 3 补充缺失值
# 得知Embared特征惨在缺失值，需要补完
print(x_train['Embarked'].value_counts())
print(x_test['Embarked'].value_counts())
# 对于类别型特征，使用出现频率最高的特征来填充，可以作为减少引入误差的方法之一
x_train['Embarked'].fillna('S', inplace=True)
x_test['Embarked'].fillna('S', inplace=True)
x_train['Age'].fillna(x_train['Age'].mean(), inplace=True)
x_test['Age'].fillna(x_test['Age'].mean(), inplace=True)
x_test['Fare'].fillna(x_test['Fare'].mean(), inplace=True)
print(x_train.info())
print(x_test.info())
# 4 采用DictVectorizer对特征向量化
dict_vectorizer = DictVectorizer(sparse=False)
x_train = dict_vectorizer.fit_transform(x_train.to_dict(orient='record'))
print(dict_vectorizer.feature_names_)
x_test = dict_vectorizer.transform(x_test.to_dict(orient='record'))
# 5 训练模型
forest_classifier = RandomForestClassifier()
xgb_classifier = XGBClassifier()
# 使用5折交叉验证的方式进行性能评估
forest_mean_score = cross_val_score(forest_classifier, x_train, y_train, cv=5).mean()
print(forest_mean_score)
xgb_mean_score = cross_val_score(xgb_classifier, x_train, y_train, cv=5).mean()
print(xgb_mean_score)
# 6 使用并行网格搜索的方式选择更好的超参组合
params = {
'max_depth': range(2, 8), 'n_estimators': range(100, 1200, 200),
'learning_rate': [0.05, 0.1, 0.25, 0.5, 1.0]
}
xgbc_best = XGBClassifier()
grid_search_cv = GridSearchCV(xgbc_best, params, n_jobs=-1, cv=5)
grid_search_cv.fit(x_train, y_train)
print(grid_search_cv.best_score_)
print(grid_search_cv.best_params_)
# 7 预测结果并写入文件
predict_result = grid_search_cv.predict(x_test)
submission_data = pandas.DataFrame({'PassengerId': test_data['PassengerId'], 'Survived': predict_result})
submission_data.to_csv('data/submission/titanic_submission.csv', index=False)
