def selectParametersForMLPC(a, b, c, d):
"""http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
http://scikit-learn.org/stable/modules/grid_search.html#grid-search"""
model = MLPC()
parameters = {
'verbose': [False],
'activation': ['logistic', 'relu'],
'max_iter': [1000, 2000],
'learning_rate': ['constant', 'adaptive']
}
accuracy_scorer = make_scorer(accuracy_score)
grid_obj = GridSearchCV(model, parameters, scoring=accuracy_scorer)
grid_obj = grid_obj.fit(a, b)
model = grid_obj.best_estimator_
model.fit(a, b)
print('Selected Parameters for Multi-Layer Perceptron NN:\n')
print(model)
print('')
# predictions = model.predict(c)
# print(accuracy_score(d, predictions))
# print('Logistic Regression - Training set accuracy: %s' % accuracy_score(d, predictions))
kfold = model_selection.KFold(n_splits=10)
accuracy = model_selection.cross_val_score(model,
a,
b,
cv=kfold,
scoring='accuracy')
mean = accuracy.mean()
stdev = accuracy.std()
print('SKlearn Multi-Layer Perceptron - Training set accuracy: %s (%s)' %
(mean, stdev))
print('')
def train(self):
classifiers = [["Rfc",
Rfc(criterion="entropy", n_estimators=100)],
["knn", Knn(10, algorithm="auto")],
["svc",
SVC(kernel="linear", C=0.025, verbose=True)],
[
"MLPC",
MLPC(activation='identity',
learning_rate_init=0.01,
hidden_layer_sizes=(3, 2, 2),
learning_rate='adaptive',
solver='adam',
verbose=True,
max_iter=100)
]]
def dump_Data(fileName, model):
try:
f = open(PathFile.PREDICRFILE + fileName + ".pkl", "wb")
pickle.dump(model, f)
f.close()
print(fileName, 'Dump_file OK...')
except IOError as e:
print(e)
for name, model in classifiers:
model.fit(self.X, self.z.values.ravel())
dump_Data(name, model)
return model
def MLPC_pack(xtrain, xtest, ytrain):
model = MLPC(hidden_layer_sizes=(5, 2),
solver='adam',
alpha=1e-5,
random_state=11)
model.fit(xtrain, ytrain)
ypre = model.predict(xtest)
return ypre
def fit_predict(self, dfit, dpre, tournament):
clf = MLPC(hidden_layer_sizes=self.p['layers'],
alpha=self.p['alpha'],
activation=self.p['activation'],
learning_rate_init=self.p['learn'],
random_state=self.p['seed'],
max_iter=200)
clf.fit(dfit.x, dfit.y[tournament])
yhat = clf.predict_proba(dpre.x)[:, 1]
return dpre.ids, yhat
def get_models(dataset):
if dataset in ["mnist12", "mnist28"]:
classifiers = [(DTC(max_depth=30, class_weight='balanced'),
"Decision Tree (max_depth=30)"),
(LRC(solver='lbfgs',
n_jobs=2,
multi_class="auto",
class_weight='balanced',
max_iter=50), "Logistic Regression"),
(MLPC((100, ), max_iter=50), "MLP (100)")]
return classifiers
if dataset in ['adult']:
classifiers = [(DTC(max_depth=15, class_weight='balanced'),
"Decision Tree (max_depth=20)"),
(ABC(), "Adaboost (estimator=50)"),
(LRC(solver='lbfgs',
n_jobs=2,
class_weight='balanced',
max_iter=50), "Logistic Regression"),
(MLPC((50, ), max_iter=50), "MLP (50)")]
return classifiers
if dataset in ['census', 'credit']:
classifiers = [
(DTC(max_depth=30,
class_weight='balanced'), "Decision Tree (max_depth=30)"),
(ABC(), "Adaboost (estimator=50)"),
(MLPC((100, ), max_iter=50), "MLP (100)"),
]
return classifiers
if dataset in ['intrusion', 'covtype']:
classifiers = [
(DTC(max_depth=30,
class_weight='balanced'), "Decision Tree (max_depth=30)"),
(MLPC((100, ), max_iter=50), "MLP (100)"),
]
return classifiers
if dataset in ['news']:
regressors = [(LRR(), "Linear Regression"),
(MLPR((100, ), max_iter=50), "MLP (100)")]
return regressors
assert 0
def MLPClassifier(trainData, trainLable, testData, testLable):
clf = MLPC(solver='adam',
activation='relu',
alpha=1e-4,
random_state=1,
max_iter=200,
learning_rate_init=.1)
clf.fit(trainData, trainLable)
pickle.dump(clf, open('./models/MLPClassifier.pkl', 'wb'))
predict = clf.predict(testData)
return calculateScores(testLable, predict)
def runVotingClassifier(a, b, c, d):
"""http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.VotingClassifier.html
http://scikit-learn.org/stable/modules/ensemble.html#voting-classifier"""
global votingC, mean, stdev # eventually I should get rid of these global variables and use classes instead. in this case i need these variables for the submission function.
votingC = VotingClassifier(estimators=[('LSVM',
LinearSVC(C=0.0001,
class_weight=None,
dual=True,
fit_intercept=True,
intercept_scaling=1,
loss='squared_hinge',
max_iter=1000,
multi_class='ovr',
penalty='l2',
random_state=None,
tol=0.0001,
verbose=0)),
('MLPC',
MLPC(activation='logistic',
alpha=0.0001,
batch_size='auto',
beta_1=0.9,
beta_2=0.999,
early_stopping=False,
epsilon=1e-08,
hidden_layer_sizes=(100, ),
learning_rate='constant',
learning_rate_init=0.001,
max_iter=2000,
momentum=0.9,
nesterovs_momentum=True,
power_t=0.5,
random_state=None,
shuffle=True,
solver='adam',
tol=0.0001,
validation_fraction=0.1,
verbose=False,
warm_start=False))],
voting='hard')
votingC = votingC.fit(a, b)
kfold = model_selection.KFold(n_splits=10)
accuracy = model_selection.cross_val_score(votingC,
a,
b,
cv=kfold,
scoring='accuracy')
meanC = accuracy.mean()
stdevC = accuracy.std()
print('Ensemble Voting Method - Training set accuracy: %s (%s)' %
(meanC, stdevC))
print('')
return votingC, meanC, stdevC
def learnData(xData,yData,f_obj,MLtype):
f_obj.write('Accuracy for {}:\n'.format(MLtype))
for test in [0.10,0.15,0.20,0.25]:
xData_train,xData_test,yData_train,yData_test = tts(xData,yData,test_size=test,random_state=42)
if(MLtype=='LSVC'): clf = LSVC()
if(MLtype=='LR'): clf = LR()
if(MLtype=='MNB'): clf = MNB()
else: clf = MLPC()
clf.fit(xData_train,yData_train)
score = clf.score(xData_test,yData_test)
f_obj.write('\ttest partition {} yields {} accuracy\n'.format(test,score))
f_obj.write('\n')
def runMLPC(a, b, c, d):
classifier = MLPC(activation='relu', max_iter=1000)
classifier.fit(a, b)
kfold = model_selection.KFold(n_splits=10)
accuracy = model_selection.cross_val_score(classifier,
a,
b,
cv=kfold,
scoring='accuracy')
mean = accuracy.mean()
stdev = accuracy.std()
print(
'SKlearn Multi-layer Perceptron NN - Training set accuracy: %s (%s)' %
(mean, stdev))
print('')
def __init__(
self,
hidden_layer_sizes=(100, ),
activation='relu', # ‘identity’, ‘logistic’, ‘tanh’, ‘relu’
solver="adam", # ‘lbfgs’, ‘sgd’, ‘adam’
alpha=0.0001, # l2 penalty param
batch_size="auto",
learning_rate="constant", # ‘constant’, ‘invscaling’, ‘adaptive’
learning_rate_init=0.001,
power_t=0.5, # exponent for exp. lr decay
max_iter=200, # max epochs
shuffle=True, # shuffle batches
tol=1e-4, # tolerance for loss/score decrease/increase
momentum=0.9, # momentum for sgd
nesterovs_momentum=True,
early_stopping=False, # stops when n_iter_no_change epochs yielded no increase
n_iter_no_change=10,
validation_fraction=0.1, # percentage of training set used for validation
beta_1=0.9, # beta1 for adam
beta_2=0.999, # beta2 for adam
epsilon=1e-8 # adam numerical stability
):
self._clf = MLPC(hidden_layer_sizes=hidden_layer_sizes,
activation=activation,
solver=solver,
alpha=alpha,
batch_size=batch_size,
learning_rate=learning_rate,
learning_rate_init=learning_rate_init,
power_t=power_t,
max_iter=max_iter,
shuffle=shuffle,
tol=tol,
early_stopping=early_stopping,
n_iter_no_change=n_iter_no_change,
momentum=momentum,
nesterovs_momentum=nesterovs_momentum,
validation_fraction=validation_fraction,
beta_1=beta_1,
beta_2=beta_2,
epsilon=epsilon)
self._last_score = None
self._model_path = None
def __init__(self,
hidden_layer_sizes=100,
activation='relu',
solver='adam',
alpha=0.0001,
batch_size='auto',
learning_rate='constant',
learning_rate_init=0.001,
power_t=0.5,
max_iter=200,
shuffle=True,
random_state=None,
tol=0.0001,
warm_start=False,
momentum=0.9,
nesterovs_momentum=True,
early_stopping=False,
validation_fraction=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
n_iter_no_change=10,
max_fun=15000):
self.tol = tol
self.n_iter_no_change = n_iter_no_change
self.momentum = momentum
self.nesterovs_momentum = nesterovs_momentum
self.beta_2 = beta_2
self.hidden_layer_sizes = hidden_layer_sizes
self.alpha = alpha
self.max_fun = max_fun
self.beta_1 = beta_1
self.activation = activation
self.early_stopping = early_stopping
self.epsilon = epsilon
self.warm_start = warm_start
self.learning_rate_init = learning_rate_init
self.solver = solver
self.shuffle = shuffle
self.random_state = random_state
self.max_iter = max_iter
self.batch_size = batch_size
self.validation_fraction = validation_fraction
self.learning_rate = learning_rate
self.power_t = power_t
self.model = MLPC(validation_fraction=self.validation_fraction,
activation=self.activation,
learning_rate=self.learning_rate,
alpha=self.alpha,
beta_1=self.beta_1,
solver=self.solver,
learning_rate_init=self.learning_rate_init,
hidden_layer_sizes=self.hidden_layer_sizes,
epsilon=self.epsilon,
nesterovs_momentum=self.nesterovs_momentum,
max_fun=self.max_fun,
momentum=self.momentum,
shuffle=self.shuffle,
n_iter_no_change=self.n_iter_no_change,
early_stopping=self.early_stopping,
power_t=self.power_t,
beta_2=self.beta_2,
warm_start=self.warm_start,
tol=self.tol,
batch_size=self.batch_size,
max_iter=self.max_iter,
random_state=self.random_state)
def __init__(self, featureset=None, target=None, mode='predict', path=''):
if (mode == 'train'):
self.__svm = SVC(C=1.0,
cache_size=200,
class_weight=None,
coef0=0.0,
decision_function_shape='ovr',
degree=3,
gamma='auto',
kernel='rbf',
max_iter=-1,
probability=False,
random_state=None,
shrinking=True,
tol=0.001,
verbose=False)
self.__svr = SVR(C=1.0,
cache_size=200,
coef0=0.0,
degree=3,
epsilon=0.1,
gamma='auto',
kernel='rbf',
max_iter=-1,
shrinking=True,
tol=0.001,
verbose=False)
self.__nusvm = NuSVC(cache_size=200,
class_weight=None,
coef0=0.0,
decision_function_shape='ovr',
degree=3,
gamma='auto',
kernel='rbf',
max_iter=-1,
nu=0.5,
probability=False,
random_state=None,
shrinking=True,
tol=0.001,
verbose=False)
self.__nusvr = NuSVR(C=1.0,
cache_size=200,
coef0=0.0,
degree=3,
gamma='auto',
kernel='rbf',
max_iter=-1,
nu=0.5,
shrinking=True,
tol=0.001,
verbose=False)
self.__linsvm = LinearSVC(C=1.0,
class_weight=None,
dual=True,
fit_intercept=True,
intercept_scaling=1,
loss='squared_hinge',
max_iter=1000,
multi_class='ovr',
penalty='l2',
random_state=None,
tol=0.0001,
verbose=0)
self.__linsvr = LinearSVR(C=1.0,
dual=True,
epsilon=0.0,
fit_intercept=True,
intercept_scaling=1.0,
loss='epsilon_insensitive',
max_iter=1000,
random_state=None,
tol=0.0001,
verbose=0)
self.__mlpc = MLPC(activation='relu',
alpha=1e-05,
batch_size='auto',
beta_1=0.9,
beta_2=0.999,
early_stopping=False,
epsilon=1e-08,
hidden_layer_sizes=(100, 25),
learning_rate='constant',
learning_rate_init=0.001,
max_iter=200,
momentum=0.9,
nesterovs_momentum=True,
power_t=0.5,
random_state=1,
shuffle=True,
solver='lbfgs',
tol=0.0001,
validation_fraction=0.1,
verbose=False,
warm_start=False)
self.__mlpr = MLPR(activation='relu',
alpha=0.0001,
batch_size='auto',
beta_1=0.9,
beta_2=0.999,
early_stopping=False,
epsilon=1e-08,
hidden_layer_sizes=(100, 25),
learning_rate='constant',
learning_rate_init=0.001,
max_iter=200,
momentum=0.9,
nesterovs_momentum=True,
power_t=0.5,
random_state=None,
shuffle=True,
solver='adam',
tol=0.0001,
validation_fraction=0.1,
verbose=False,
warm_start=False)
self.__dtc = DTC(class_weight=None,
criterion='gini',
max_depth=None,
max_features=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
presort=False,
random_state=None,
splitter='best')
self.__dtr = DTR(criterion='mse',
max_depth=None,
max_features=None,
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
presort=False,
random_state=None,
splitter='best')
self.__rfc = RFC(bootstrap=True,
class_weight=None,
criterion='gini',
max_depth=100,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators=50,
n_jobs=1,
oob_score=False,
random_state=None,
verbose=0,
warm_start=False)
self.__rfr = RFR(bootstrap=True,
criterion='mse',
max_depth=None,
max_features='auto',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators=10,
n_jobs=1,
oob_score=False,
random_state=None,
verbose=0,
warm_start=False)
(self.__svm, self.__svr, self.__nusvm, self.__nusvr, self.__linsvm,
self.__linsvr, self.__mlpc, self.__mlpr, self.__dtc, self.__dtr,
self.__rfc, self.__rfr) = self.__trainAll(X=list(featureset),
Y=list(target))
self.__saveModelsToFile(path)
else:
self.__svm = joblib.load(path + 'Mel_SVM.pkl')
self.__svr = joblib.load(path + 'Mel_SVR.pkl')
self.__nusvm = joblib.load(path + 'Mel_NuSVM.pkl')
self.__nusvr = joblib.load(path + 'Mel_NuSVR.pkl')
self.__linsvm = joblib.load(path + 'Mel_LinSVM.pkl')
self.__linsvr = joblib.load(path + 'Mel_LinSVR.pkl')
self.__mlpc = joblib.load(path + 'Mel_MLPC.pkl')
self.__mlpr = joblib.load(path + 'Mel_MLPR.pkl')
self.__dtc = joblib.load(path + 'Mel_DTC.pkl')
self.__dtr = joblib.load(path + 'Mel_DTR.pkl')
self.__rfc = joblib.load(path + 'Mel_RFC.pkl')
self.__rfr = joblib.load(path + 'Mel_RFR.pkl')
"""
#####RFC pipes#####
"""
rfc_norm_pipe = mp(MinMaxScaler(), RFC(random_state=47))
rfc_stand_pipe = mp(StandardScaler(), RFC(random_state=47))
rfc_pca_pipe = mp(PCA(), RFC())
"""
#####SVC pipes#####
"""
svc_norm_pipe = mp(MinMaxScaler(), SVC())
svc_stand_pipe = mp(StandardScaler(), SVC())
svc_pca_pipe = mp(PCA(), SVC())
"""
#####MLPC pipes#####
"""
mlpc_norm_pipe = mp(MinMaxScaler(), MLPC(random_state=47))
mlpc_stand_pipe = mp(StandardScaler(), MLPC(random_state=47))
mlpc_pca_pipe = mp(PCA(), MLPC())
"""
#####kNN grid#####
"""
kNN_param_grid = {
'kneighborsclassifier__n_neighbors': [1, 2, 3, 4, 5],
'kneighborsclassifier__weights': ['uniform', 'distance'],
'kneighborsclassifier__p': [1, 2, 3]
}
"""
Test set score: 0.12
Best parameters: {'kneighborsclassifier__n_neighbors': 1, 'kneighborsclassifier__p': 1, 'kneighborsclassifier__weights': 'uniform'}
"""
kNN_norm_grid = GSCV(knn_norm_pipe, kNN_param_grid, scoring='f1', cv=5)
def fit_model(features, sumstats, train_genes, test_genes, model='logit'):
"""
Fit classifier to train_genes and calculate RMSE on test_genes
"""
all_genes = train_genes + test_genes
# Join sumstats with features for logistic regression, subset to
# genes of interest, and drop genes with NaN BFDPs
full_df = sumstats.merge(features,
how='left',
left_index=True,
right_index=True)
full_df = full_df.loc[full_df.index.isin(all_genes), :].dropna()
train_df = full_df.loc[full_df.index.isin(train_genes), :].\
drop(labels='chrom', axis=1)
test_df = full_df.loc[full_df.index.isin(test_genes), :].\
drop(labels='chrom', axis=1)
# Instantiate classifier dependent on model
if model == 'logit':
grid_params = {
'C': [10**x for x in range(-2, 3, 1)],
'l1_ratio': [x / 10 for x in range(0, 11, 1)]
}
base_class = logit(solver='saga', penalty='elasticnet')
elif model == 'svm':
grid_params = {'C': [10**x for x in range(-2, 2, 1)]}
base_class = SVC(random_state=0,
probability=True,
break_ties=True,
kernel='rbf')
elif model == 'randomforest':
grid_params = {
'n_estimators': [50, 100, 500],
'criterion': ['gini', 'entropy']
}
base_class = RFC(random_state=0, bootstrap=True, oob_score=True)
elif model == 'lda':
grid_params = {
'shrinkage': [None, 0, 0.5, 1, 'auto'],
'solver': ['svd', 'lsqr', 'eigen']
}
base_class = LDAC()
elif model == 'naivebayes':
grid_params = {'var_smoothing': [10**x for x in range(-4, -11, -1)]}
base_class = GNBC()
elif model == 'neuralnet':
grid_params = {
'hidden_layer_sizes': [(10, 5, 2), (20, 10, 5), (20, 10, 5, 2),
(50, 20, 10), (50, 20, 10, 5),
(50, 20, 10, 5, 2)],
'alpha': [10**x for x in range(-4, 5, 1)]
}
base_class = MLPC(activation='relu',
solver='adam',
early_stopping=True,
random_state=0)
elif model == 'gbdt':
grid_params = {'n_estimators': [50, 100], 'subsample': [0.5, 1]}
base_class = GBDT(random_state=0)
elif model == 'knn':
grid_params = {
'n_neighbors': [10, 50, 100, 500],
'weights': ['uniform', 'distance'],
'leaf_size': [5, 10, 25, 50, 100]
}
base_class = KNN()
# Learn best parameters for classifier using cross-validated grid search
classifier = GridSearchCV(base_class, grid_params, verbose=1, n_jobs=-1)
# Fit sklearn model & predict on test set
# (Models parameterized by grid search need to be treated separately)
if isinstance(classifier, GridSearchCV):
fitted_model = classifier.fit(train_df.drop(labels='bfdp', axis=1),
np.round(train_df.bfdp)).best_estimator_
else:
fitted_model = classifier.fit(train_df.drop(labels='bfdp', axis=1),
np.round(train_df.bfdp))
test_bfdps = pd.Series(fitted_model.predict_proba(
test_df.drop(labels='bfdp', axis=1))[:, 1],
name='pred',
index=test_df.index)
# Compute RMSE of bfdps for test set
test_vals = test_df.merge(test_bfdps, left_index=True, right_index=True).\
loc[:, 'bfdp pred'.split()]
test_rmse = rmse(test_vals.to_records(index=False))
return fitted_model, test_rmse
from sklearn.metrics import log_loss
# grid search cross validation
from sklearn.model_selection import GridSearchCV
# ignore ConverenceWarning
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings("ignore", category=ConvergenceWarning)
##################################
## 3.1 train and test models using GridSearchCV
models = {
'DT': DTC(),
'LR': LR(),
'MLP': MLPC(),
'SVC': SVC(),
'NB': NB(),
'KNN': KNNC(),
'Bagging': BaggingC(),
'RF': RFC(),
'AdaBoost': AdaBoostC(),
'GB': GBC(),
'XGB': XGB(),
}
param_dict = {
# 0.67 {'max_depth': 1, 'max_leaf_nodes': None, 'min_samples_leaf': 1, 'min_samples_split': 2}
'DT': {
'max_depth': [1,2,3,None],
'max_leaf_nodes': [4,6,8,10,None],
print(count)
X1 = X1.reshape(int(X1.shape[0] / 50), 50)
X2 = X2.reshape(int(X2.shape[0] / 50), 50)
X = np.concatenate((X1, X2), axis=1)
X = np.nan_to_num(X)
y = train_df['project_is_approved'].as_matrix()
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=.2,
random_state=47)
mlpc_norm_pipe = mp(MinMaxScaler(), MLPC(random_state=47))
mlp_param_grid1 = {
'mlpclassifier__hidden_layer_sizes': [10, 100, (10, 10), (100, 100)],
'mlpclassifier__activation': ['identity', 'logistic', 'tanh', 'relu'],
'mlpclassifier__solver': ['lbfgs', 'sgd', 'adam']
}
mlp_param_grid2 = {
'hidden_layer_sizes': [10, 100, (10, 10), (100, 100)],
'activation': ['identity', 'logistic', 'tanh', 'relu'],
'solver': ['lbfgs', 'sgd', 'adam']
}
mlp_norm_grid = GSCV(mlpc_norm_pipe, mlp_param_grid1, scoring='f1', cv=5)
mlp_norm_grid.fit(X_train, y_train)
print("Test set score: {:.2f}".format(mlp_norm_grid.score(X_test, y_test)))
from sklearn.neural_network import MLPClassifier as MLPC
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
filename = '/usr/src/app/sentiment/models/pickles/MLPC.pickle'
if isfile(filename) == False:
train, test = train_test_split(utils.read_data(), test_size=0.2)
train_embeddings = utils.combined_embeddings(train['text'].tolist())
test_embeddings = utils.combined_embeddings(test['text'].tolist())
clf = MLPC(
hidden_layer_sizes=(256),
learning_rate='adaptive',
max_iter=1000
)
clf.fit(train_embeddings, train['sentiment'])
prediction = clf.predict(test_embeddings)
report = classification_report(test['sentiment'], prediction)
print(report)
with open(filename, 'wb') as f:
pickle.dump(clf, f)
else:
print('Already Trained!')
X = data['data']
y = data['target']
# Dividindo um porcentagem de 20% do data set para test e 80% para treino
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
scaler = StandardScaler()
# Convertendo para uma escala padrao de acordo com os atributos de treino
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
# Criando a RNA com 3 layers de 100 neuronios cada uma, e com 1000 epocas
clf = MLPC(hidden_layer_sizes=(100, 100, 100), max_iter=1000)
# Criando o modelo atraves do treinamento
clf.fit(X_train, y_train)
# Classificando os atributos do teste
predictions = clf.predict(X_test)
# Calculando o erro medio quadratico
mean = mean_squared_error(y_test, predictions)
print(mean)
# Criando uma matriz de confusao
confusion_m = pd.DataFrame(confusion_matrix(y_test, predictions),
columns=['Benigno', 'Maligno'],
index=['Benigno', 'Maligno'])
for current_para in parameter_set:
best_classifier = None
highest_accuracy = 0
""" get current parameters for the classifier """
current_alpha = current_para[0]
current_activition_function = current_para[1]
current_hidden_layer = current_para[2]
current_iteration = max_iteration[3]
fold_num = 0
for i in range(0, 5):
fold_num = fold_num + 1
""" build up a classifier """
current_classifier = MLPC(hidden_layer_sizes = current_hidden_layer, activation = current_activition_function, alpha = current_alpha, max_iter = current_iteration)
""" get training and validation set """
current_training_feature, current_training_label, current_validation_feature, current_validation_label = five_folds(training_data, i)
""" training """
current_classifier.fit(current_training_feature, current_training_label)
""" validation """
correct_num = 0
number_of_instance_in_validation_set = len(current_validation_feature)
for i in range(number_of_instance_in_validation_set):
if current_classifier.predict(current_validation_feature[i].reshape(1, -1)) == current_validation_label[i]:
correct_num = correct_num + 1
print (" Correct number for this time is ", correct_num)
dtc_people.score(X_test_people_stand, y_test_people)))
###DONE###
rfc_people = RFC(n_estimators=100,
max_depth=25,
bootstrap=False,
random_state=37).fit(X_train_people_norm, y_train_people)
print("Test set score of RFC people: {:.3f}".format(
rfc_people.score(X_test_people_norm, y_test_people)))
svc_people = SVC(C=4, kernel='linear',
random_state=37).fit(X_train_people_stand, y_train_people)
print("Test set score of SVC people: {:.3f}".format(
svc_people.score(X_test_people_stand, y_test_people)))
mlpc_people = MLPC(alpha=.1, random_state=37).fit(X_train_people_nmf,
y_train_people)
print("Test set score of MLPC people: {:.3f}".format(
mlpc_people.score(X_test_people_nmf, y_test_people)))
print('people\n')
#Mnist
###DONE###
knc_mnist = KNC(weights='distance').fit(X_train_mnist_norm, y_train_mnist)
print("Test set score of kNN mnist: {:.3f}".format(
knc_mnist.score(X_test_mnist_norm, y_test_mnist)))
###DONE###
dtc_mnist = DTC(criterion='entropy',
max_depth=15,
min_samples_split=3,
random_state=37).fit(X_train_mnist_nmf, y_train_mnist)
#"DTC",
#"GNB",
"QDA"]
model_types = [LR,
RFC,
#ABC,
MLPC,
KNC,
SVC,
#DTC,
#GNB,
QDA]
models = [LR(),
RFC(n_estimators=30),
#ABC(),
MLPC(),
KNC(),
SVC(probability=True),
#DTC(),
#GNB(),
QDA()]
models2 = copy.deepcopy(models)
### experiment bright students math finance
N = 10000 ## 1000 of each group (groups S and T)
minority_percent = 0.3
MIN = int(minority_percent * N)
MAJ = int((1 - minority_percent) * N)
# print(MIN, MAJ)
from sklearn.neural_network import MLPClassifier as MLPC
# The data set from MMNIST
images, labels = load_mnist()
images = images[:1000]
labels = labels[:1000]
# To apply a classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(images)
data = images.reshape((n_samples, -1))
print(data[3])
# Create a classifier: a support vector classifier
classifier = MLPC()
labels = np.ravel(labels)
# We learn the digits on the first half of the digits
classifier.fit(data[:n_samples / 2], labels[:n_samples / 2])
# Now predict the value of the digit on the second half:
expected = labels[n_samples / 2:]
predicted = classifier.predict(data[n_samples / 2:])
print("Classification report for classifier %s:\n%s\n" %
(classifier, metrics.classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))
"""
images_and_predictions = list(zip(digits.images[n_samples / 2:], predicted))
