def readme():
data = sklearn.datasets.load_digits()
X = data["data"]
y = data["target"]
paramgrid = {
"kernel": ["rbf"],
"C": np.logspace(-9, 9, num=25, base=10),
"gamma": np.logspace(-9, 9, num=25, base=10)
}
random.seed(1)
cv = EvolutionaryAlgorithmSearchCV(estimator=SVC(),
params=paramgrid,
scoring="accuracy",
cv=StratifiedKFold(n_splits=4),
verbose=1,
population_size=10,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=5)
cv.fit(X, y)
return cv
def main():
df_train = pd.read_csv('../train_dataset.csv')
df_test = pd.read_csv('../test_dataset.csv')
X_train, y_train = df_train.iloc[:, 2:].values, df_train.iloc[:, 0].values
#X_test, y_test = df_test.iloc[:, 2:].values, df_test.iloc[:, 0].values
# log-uniform: understand as search over p = exp(x) by varying x
opt = EvolutionaryAlgorithmSearchCV(
estimator=SVC(),
# ref: https://github.com/automl/auto-sklearn/blob/master/autosklearn/pipeline/components/classification/libsvm_svc.py
params={
"kernel": ["rbf"],
"C": np.logspace(1e-6, 1e+6, num=20, base=10),
"gamma": np.logspace(3.0517578125e-05, 8, num=20, base=10),
'decision_function_shape': ['ovo', 'ovr'],
'degree': list(range(2, 5)),
'coef0': np.logspace(-1, 1, num=20, base=10),
'coef0': np.logspace(1e-5, 1e-1, num=20, base=10),
},
cv=StratifiedKFold(n_splits=10, shuffle=True),
scoring="accuracy",
verbose=True,
population_size=50,
gene_mutation_prob=0.10,
tournament_size=3,
generations_number=10,
)
opt.fit(X_train, y_train)
def svm_ga(X, y, rfe=True, paramgrid=None):
# feature selection
fltr = RFE(ReliefF(), n_features_to_select=5,
step=0.5) if rfe else ReliefF(n_features_to_select=5,
n_neighbors=3)
clf = SVC()
param_grid = {
"svc__kernel": ["rbf"],
'svc__C': [10e-2, 10e-1, 10, 10e1, 10e2, 10e3, 10e4],
'svc__gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 1, 1.1]
} if paramgrid is None else paramgrid
# make pipeline
pipe = make_pipeline(preprocessing.StandardScaler(), fltr, clf)
from evolutionary_search import EvolutionaryAlgorithmSearchCV
cv = EvolutionaryAlgorithmSearchCV(estimator=pipe,
params=param_grid,
scoring="accuracy",
cv=10,
verbose=1,
population_size=50,
gene_mutation_prob=0.1,
gene_crossover_prob=0.8,
tournament_size=10,
generations_number=25)
cv.fit(X, y)
print(cv.best_params_)
print(cv.best_score_)
def get_GeneticGridSearchCV(model, params, X, y):
from evolutionary_search import EvolutionaryAlgorithmSearchCV
print("performing genetic grid search ...")
grid = EvolutionaryAlgorithmSearchCV(estimator=model,
params=params,
scoring="r2",
# cv=StratifiedKFold(n_splits=2),
verbose=True,
population_size=50,
gene_mutation_prob=0.10,
tournament_size=3,
generations_number=10,
# pmap = pool.map,
)
grid.fit(X, y.ravel()) # fit the model and parameters
# our classical metric for performance
print("Best Accuracy: {}".format(grid.best_score_))
# the best parameters that caused the best accuracy
print("Best Parameters: {}".format(grid.best_params_))
# the average time it took a model to fit to the data (in seconds)
print("Average Time to Fit (s): {}".format(round(grid.cv_results_['mean_fit_time'].mean(), 3)))
# the average time it took a model to predict out of sample data (in seconds)
# this metric gives us insight into how this model will perform in real-time analysis
print("Average Time to Score (s): {}".format(round(grid.cv_results_['mean_score_time'].mean(), 3)))
print(pd.DataFrame(grid.cv_results_).sort_values("mean_test_score", ascending=False).head())
def evo_search(xtrain, xtest, ytrain, ytest):
layers = [[a, a] for a in range(10, 500, 100)]
print(layers)
parameters = {
'activation': ['identity', 'logistic', 'tanh', 'relu'],
# 'solver': ['lbfgs', 'sgd', 'adam'],
# 'learning_rate': ['constant', 'invscaling', 'adaptive'],
'batch_size': [5, 10, 20, 50, 100],
'learning_rate_init': [0.0001, 0.001, 0.01, 0.1],
# 'hidden_layer_sizes': generate_networks(),
'hidden_layer_sizes': layers
}
print(parameters)
print('Starting evolutionary search')
cv = EvolutionaryAlgorithmSearchCV(estimator=MLPClassifier(random_state=42, max_iter=20000),
params=parameters,
scoring=make_scorer(f1_score),
#cv=StratifiedKFold(n_splits=4),
verbose=10,
population_size=20,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=10,
n_jobs=1)
cv.fit(xtrain, ytrain.values.ravel())
print_classifier_stats(cv.best_estimator_, xtrain, xtest, ytrain, ytest)
print('Evo search done...')
def evolution_method(self):
# this does not work, but we need to continue
params = dict(epochs=[200], batch_size=[4, 8])
# cv = [(slice(None), slice(None))]
es = self.callback.es
mc = self.callback.mc
tb = self.callback.tb
my_callbacks = [es, mc]
fit_params = {
"epochs":
300,
"validation_data":
(self.data_obj.x_validation, self.data_obj.y_validation),
"callbacks":
my_callbacks
}
self.evo = EvolutionaryAlgorithmSearchCV(
estimator=self.keras_regressor,
params=params,
verbose=0,
population_size=10,
fit_params=fit_params)
evo_hist = self.evo.fit(X=self.data_obj.x_train,
y=self.data_obj.y_train)
return evo_hist
def SVM(X_train_little,
y_train_little,
X_train_pca,
X_test_pca,
y_train,
y_test,
tune_only=False):
from sklearn.svm import SVC
from sklearn.model_selection import cross_validate
C_range = np.linspace(1, 10, 101)
gamma_range = np.linspace(3000, 4000, 100)
param_dist = dict(gamma=gamma_range, C=C_range)
cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=SEED)
rnds = EvolutionaryAlgorithmSearchCV(estimator=SVC(max_iter=200),
params=param_dist,
scoring="f1",
cv=cv,
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=6,
n_jobs=4)
rnds.fit(X_train_little, y_train_little)
# summarize the results of the random parameter search
file = open("SVM_best_parameters.txt", "w")
file.write("{}\n".format(rnds.best_score_))
file.write('C: {}\n'.format(rnds.best_estimator_.C))
file.write('gamma: {}\n'.format(rnds.best_estimator_.gamma))
file.close()
if not tune_only:
# apply best parameters
svc = SVC(max_iter=200,
C=rnds.best_estimator_.C,
gamma=rnds.best_estimator_.gamma,
random_state=SEED)
svc.fit(X_train_pca, y_train)
sc_tr = cross_validate(svc,
X_train_pca,
y_train,
scoring=SCORING,
cv=5,
return_train_score=False)
sc_ts = cross_validate(svc,
X_test_pca,
y_test,
scoring=SCORING,
cv=5,
return_train_score=False)
pred = svc.predict(X_test_pca)
pred_train = svc.predict(X_train_pca)
output_report("SVM", y_train, pred_train, y_test, pred, sc_tr, sc_ts)
def LR2(X_train_little,
y_train_little,
X_train_pca,
X_test_pca,
y_train,
y_test,
tune_only=False):
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_validate
C_range = np.linspace(1, 50, 50)
tol_range = np.linspace(0.001, 0.01, 50)
param_dist = dict(tol=tol_range, C=C_range)
cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=SEED)
rnds = EvolutionaryAlgorithmSearchCV(
estimator=LogisticRegression(penalty='l2'),
params=param_dist,
scoring="f1",
cv=cv,
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=6,
n_jobs=4)
rnds.fit(X_train_little, y_train_little)
# summarize the results of the random parameter search
file = open("LR2_best_parameters.txt", "w")
file.write("{}\n".format(rnds.best_score_))
file.write('C: {}\n'.format(rnds.best_estimator_.C))
file.write('tol: {}\n'.format(rnds.best_estimator_.tol))
file.close()
if not tune_only:
# apply best parameters
l2r = LogisticRegression(C=rnds.best_estimator_.C,
tol=rnds.best_estimator_.tol,
random_state=SEED)
l2r.fit(X_train_pca, y_train)
sc_tr = cross_validate(l2r,
X_train_pca,
y_train,
scoring=SCORING,
cv=5,
return_train_score=False)
sc_ts = cross_validate(l2r,
X_test_pca,
y_test,
scoring=SCORING,
cv=5,
return_train_score=False)
pred = l2r.predict(X_test_pca)
pred_train = l2r.predict(X_train_pca)
output_report("LR2", y_train, pred_train, y_test, pred, sc_tr, sc_ts)
def sk_params_search_best(
clf,
X,
y,
param_grid={"alpha": np.linspace(0, 1, 5)},
method="gridsearch",
param_search={
"scorename": "r2",
"cv": 5,
"population_size": 5,
"generations_number": 3
},
):
"""
Genetic: population_size=5, ngene_mutation_prob=0.10,,gene_crossover_prob=0.5, tournament_size=3, generations_number=3
:param X:
:param y:
:param clf:
:param param_grid:
:param method:
:param param_search:
:return:
"""
p = param_search
myscore = sk_score_get(p["scorename"])
if method == "gridsearch":
from sklearn.model_selection import GridSearchCV
grid = GridSearchCV(clf, param_grid, cv=p["cv"], scoring=myscore)
grid.fit(X, y)
return grid.best_score_, grid.best_params_
if method == "genetic":
from evolutionary_search import EvolutionaryAlgorithmSearchCV
from sklearn.model_selection import StratifiedKFold
# paramgrid = {"alpha": np.linspace(0,1, 20) , "l1_ratio": np.linspace(0,1, 20) }
cv = EvolutionaryAlgorithmSearchCV(
estimator=clf,
params=param_grid,
scoring=myscore,
cv=StratifiedKFold(y),
verbose=True,
population_size=p["population_size"],
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=p["generations_number"],
)
cv.fit(X, y)
return cv.best_score_, cv.best_params_
def ev_tree(self):
ev_params = self.default_evparams
ev_params['estimator'] = RandomForestClassifier()
ev_params['params'] = self.frst_space
cv = EvolutionaryAlgorithmSearchCV(**ev_params)
cv.fit(self.X_insample, self.y_insample)
clf = cv.best_estimator_
self.frst_called = True
self.opt_frst = clf
def ev_svm(self):
ev_params = self.default_evparams
ev_params['estimator'] = SVC(probability=True)
ev_params['params'] = self.svm_space
cv = EvolutionaryAlgorithmSearchCV(**ev_params)
cv.fit(self.X_insample, self.y_insample)
clf = cv.best_estimator_
self.svm_called = True
self.opt_svm = clf
def NB(X_train_little,
y_train_little,
X_train_pca,
X_test_pca,
y_train,
y_test,
tune_only=False):
from sklearn.naive_bayes import BernoulliNB
from sklearn.model_selection import cross_validate
alpha_range = np.linspace(0, 500, 500)
param_dist = dict(alpha=alpha_range)
cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2)
rnds = EvolutionaryAlgorithmSearchCV(estimator=BernoulliNB(),
params=param_dist,
scoring="f1",
cv=cv,
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=6,
n_jobs=4)
rnds.fit(X_train_little, y_train_little)
# summarize the results of the random parameter search
file = open("NB_best_parameters.txt", "w")
file.write("{}\n".format(rnds.best_score_))
file.write('alpha: {}\n'.format(rnds.best_estimator_.alpha))
file.close()
if not tune_only:
# apply best parameters
gnb = BernoulliNB(alpha=rnds.best_estimator_.alpha)
gnb.fit(X_train_pca, y_train)
sc_tr = cross_validate(gnb,
X_train_pca,
y_train,
scoring=SCORING,
cv=5,
return_train_score=False)
sc_ts = cross_validate(gnb,
X_test_pca,
y_test,
scoring=SCORING,
cv=5,
return_train_score=False)
pred = gnb.predict(X_test_pca)
pred_train = gnb.predict(X_train_pca)
output_report("NB", y_train, pred_train, y_test, pred, sc_tr, sc_ts)
def geneticGridTest(self):
print("performing Genetic grid search...")
gridSearch = EvolutionaryAlgorithmSearchCV(estimator=self.classifier,
params=self.gridParams,
cv=self.kfold,
scoring='accuracy',
verbose=True,
iid='False',
n_jobs=4,
population_size=20,
gene_mutation_prob=0.30,
tournament_size=2,
generations_number=5)
gridSearch.fit(self.X, self.y)
def GA_tune_lgbm(cls, x, y):
tuner = EvolutionaryAlgorithmSearchCV(
estimator=LGBMClassifier(),
params=cls.lgbm_paramgrid,
scoring="accuracy",
cv=TimeSeriesSplit(n_splits=4),
verbose=1,
population_size=50,
gene_mutation_prob=0.2,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=20,
)
tuner.fit(x, y)
return tuner.best_params_
def RF_DT(X_train_little, y_train_little, X_train_pca, X_test_pca, y_train, y_test, tune_only=False):
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_validate
min_samples_leaf_range = np.round(np.linspace(1, 10, 10)).astype(int)
max_depth_range = np.round(np.linspace(1, 30, 30)).astype(int)
param_dist = dict(min_samples_leaf=min_samples_leaf_range, max_depth=max_depth_range)
num_features = len(X_train_little[0])
cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=SEED)
rnds = EvolutionaryAlgorithmSearchCV( estimator = RandomForestClassifier(n_estimators=int((1+num_features/2))),
params = param_dist,
scoring = "f1",
cv = cv,
verbose = 1,
population_size = 50,
gene_mutation_prob = 0.10,
gene_crossover_prob = 0.5,
tournament_size = 3,
generations_number = 6,
n_jobs = 4)
rnds.fit(X_train_little, y_train_little)
# summarize the results of the random parameter search
file = open("RF_DT_best_parameters.txt","w")
file.write("{}\n".format(rnds.best_score_))
file.write('min_samples_leaf: {}\n'.format(rnds.best_estimator_.min_samples_leaf))
file.write('max_depth: {}\n'.format(rnds.best_estimator_.max_depth))
file.close()
if not tune_only:
# apply best parameters RF
rfc = RandomForestClassifier(n_estimators = int((1+num_features/2)),
min_samples_leaf = rnds.best_estimator_.min_samples_leaf,
max_depth = rnds.best_estimator_.max_depth,
random_state = SEED)
rfc.fit(X_train_pca,y_train)
sc_tr = cross_validate(rfc, X_train_pca, y_train, scoring=SCORING, cv=5, return_train_score=False)
sc_ts = cross_validate(rfc, X_test_pca, y_test, scoring=SCORING, cv=5, return_train_score=False)
pred = rfc.predict(X_test_pca)
pred_train = rfc.predict(X_train_pca)
output_report("RF", y_train, pred_train, y_test, pred, sc_tr, sc_ts)
return pred, pred_train
def tune(model, X, y, cv):
C = np.round(np.linspace(1, 10, 10)).astype(int)
param_dist = dict(C=C, )
#num_features = len(X[0])
best_model = EvolutionaryAlgorithmSearchCV(estimator=model,
params=param_dist,
scoring="f1_weighted",
cv=cv,
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=6,
n_jobs=4)
best_model.fit(X, y)
return best_model
def main():
paramgrid = {"kernel": ["rbf"],
"C" : np.logspace(-9, 9, num=25, base=10),
"gamma" : np.logspace(-9, 9, num=25, base=10)}
random.seed(1)
from evolutionary_search import EvolutionaryAlgorithmSearchCV
cv = EvolutionaryAlgorithmSearchCV(estimator=SVC(),
params=paramgrid,
scoring="accuracy",
cv=StratifiedKFold(n_splits=4),
verbose=1,
population_size=5,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=5,
n_jobs=4)
cv.fit(X, y)
def tune(model, X, y, cv): min_samples_leaf_range = np.round(np.linspace(1, 10, 10)).astype(int) max_depth_range = np.round(np.linspace(1, 30, 30)).astype(int) param_dist = dict(min_samples_leaf=min_samples_leaf_range, max_depth=max_depth_range) best_model = EvolutionaryAlgorithmSearchCV( estimator = model, params = param_dist, scoring = "f1_weighted", cv = cv, verbose = 1, population_size = 50, gene_mutation_prob = 0.10, gene_crossover_prob = 0.5, tournament_size = 3, generations_number = 6, n_jobs = 4) best_model.fit(X, y) return best_model
def get_search(type, pipeline_configuration, hp_metric):
fu_pl, clf_pl = pipeline_configuration.pipelines()
if type == 'grid':
return GridSearchCV(
clf_pl,
pipeline_configuration.parameters('grid'),
scoring=hp_metric,
cv=pipeline_configuration.pipeline_parameters_grid_n_splits,
refit=False,
n_jobs=-1,
verbose=1), fu_pl
elif type == 'randomized':
return RandomizedSearchCV(
clf_pl,
pipeline_configuration.parameters('randomized'),
scoring=hp_metric,
random_state=pipeline_configuration.
pipeline_parameters_randomized_random_state,
n_iter=pipeline_configuration.
pipeline_parameters_randomized_n_iter,
cv=pipeline_configuration.pipeline_parameters_randomized_n_splits,
refit=False,
n_jobs=3,
#pre_dispatch=6,
verbose=1), fu_pl
elif type == 'evolutionary':
from random import seed
seed(pipeline_configuration.
pipeline_parameters_evolutionary_random_seed)
return EvolutionaryAlgorithmSearchCV(
clf_pl,
pipeline_configuration.parameters('evolutionary'),
scoring=hp_metric,
cv=pipeline_configuration.
pipeline_parameters_evolutionary_n_splits,
population_size=pipeline_configuration.
pipeline_parameters_evolutionary_population_size,
gene_mutation_prob=pipeline_configuration.
pipeline_parameters_evolutionary_gene_mutation_prob,
gene_crossover_prob=pipeline_configuration.
pipeline_parameters_evolutionary_gene_crossover_prob,
tournament_size=pipeline_configuration.
pipeline_parameters_evolutionary_tournament_size,
generations_number=pipeline_configuration.
pipeline_parameters_evolutionary_generations_number,
refit=False,
n_jobs=-1,
verbose=1), fu_pl
def grid_search(clf,params,scoring,search_method='ev'):
if search_method == 'grid':
gs=GridSearchCV(clf,param_grid=params,scoring=scoring,cv=2,refit=True,n_jobs=2,verbose=2)
elif search_method == 'ev':
gs=EvolutionaryAlgorithmSearchCV(clf,params=params,scoring=scoring,cv=2,refit=True,n_jobs=2,verbose=2,
population_size=8,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=4
)
else:
gs=clf
gs.fit(train_x,train_y)
return gs
def cv_optimize(clf, parameters, X, y, n_jobs=1, n_folds=5, score_func=None, evo=None, population_size=5):
if score_func:
if evo:
gs = EvolutionaryAlgorithmSearchCV(pipeline, grid=parameters, scoring=score_func, n_jobs=n_jobs, population_size=population_size)
else:
gs = GridSearchCV(clf, param_grid=parameters, cv=n_folds, n_jobs=n_jobs, scoring=score_func)
else:
if evo:
gs = EvolutionaryAlgorithmSearchCV(pipeline, grid=parameters, scoring=None, verbose=True, n_jobs=4, population_size=population_size)
else:
gs = GridSearchCV(clf, param_grid=parameters, n_jobs=n_jobs, cv=n_folds)
gs.fit(X, y)
print "BEST", gs.best_params_, gs.best_score_, gs.grid_scores_
best = gs.best_estimator_
return best
for element in columnsSJ:
del train_SJ[element]
del test_SJ[element]
for element in columnsIQ:
del train_IQ[element]
del test_IQ[element]
paramgrid = {"max_depth": range(1, 10), "criterion": ["mae"]}
paramgrid2 = {"max_depth": range(1, 10), "criterion": ["mae"]}
rtreeForSJ = EvolutionaryAlgorithmSearchCV(
estimator=RandomForestRegressor(),
params=paramgrid,
scoring="neg_mean_absolute_error",
cv=StratifiedKFold(n_splits=4),
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=5,
n_jobs=1)
rtreeForIQ = EvolutionaryAlgorithmSearchCV(
estimator=RandomForestRegressor(),
params=paramgrid2,
scoring="neg_mean_absolute_error",
cv=StratifiedKFold(n_splits=4),
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
def main():
rand_st = 42
classes = ["A", "B", "C", "D", "E", "F", "G", "H"]
from itertools import combinations
subsets = []
for subset in combinations(classes, 2):
subsets.append(subset)
try:
os.makedirs(PREPROCESS_PATH + str(subset[0] + subset[1]))
except:
pass
for sub in subsets:
PATH = "/home/bruno/base-wipo/preprocess-artigo/" + str(sub[0]) + str(
sub[1]) + "/"
print(" --------------------------" + str(sub[0]) + str(sub[1]) +
"--------------------------------- ")
treinamento = "treinamento.csv"
y = pd.read_csv(os.path.join(os.path.dirname(__file__),
PATH + treinamento),
header=0,
delimiter=";",
usecols=["section"],
quoting=3)
'''
X = pd.read_csv(os.path.join(os.path.dirname(__file__),PATH+treinamento),
header=0,delimiter=";",usecols=["data"], quoting=3)
X = X["data"].tolist()
'''
X = TideneIterCSVGA(PATH + treinamento)
tfidf_transformer = TfidfVectorizer()
n = len(y)
random.seed(1)
from evolutionary_search import EvolutionaryAlgorithmSearchCV
'''
#--------------------------------- GA-RF ---------------------------------
from evolutionary_search import EvolutionaryAlgorithmSearchCV
clf_RF_gs = RandomForestClassifier(random_state=rand_st, n_jobs=-1)
clf_RF_pg = [{
'max_depth': np.logspace(0.3,4,num = 10 ,base=10,dtype='int'), #[1, 5, 13, 34, 87, 226, 584, 1505, 3880, 10000]
'n_estimators' : np.logspace(0.1,3,num = 10 ,base=10,dtype='int'), #[1, 2, 5, 11, 24, 51, 107, 226, 476, 1000]
'min_samples_split' : np.logspace(0.4, 1, num=5, base=10, dtype='int'), #[2, 3, 5, 7, 10]
'min_samples_leaf' : np.logspace(0.1,1,num = 4 ,base=9,dtype='int'), #[1, 2, 4, 9]
'max_features' : ['auto', None]
}]
model_name = "100features_40minwords_10context"
model = gensim.models.Word2Vec.load(model_name)
w2v = {w: vec for w, vec in zip(model.wv.index2word, model.wv.syn0)}
cv = EvolutionaryAlgorithmSearchCV(estimator=clf_RF_gs,
params=clf_RF_pg,
scoring="accuracy",
cv=StratifiedKFold(n_splits=4),
verbose=1,
population_size=10,
gene_mutation_prob=0.05,
gene_crossover_prob=0.25,
tournament_size=2,
generations_number=3,
n_jobs=2)
#print(model.wv.most_similar('sensitive'))
cv.fit(tfidf_transformer.fit_transform(X), y['section'].tolist())
#cv.fit(MeanEmbeddingVectorizer(w2v).transform(X), y['section'].tolist())
'''
#--------------------------------- GA-SVC ---------------------------------
paramgrid = {"C": np.logspace(-9, 9, num=25, base=10)}
cv = EvolutionaryAlgorithmSearchCV(estimator=LinearSVC(),
params=paramgrid,
scoring="accuracy",
cv=StratifiedKFold(n_splits=4),
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=5,
n_jobs=4)
out = cv.fit(tfidf_transformer.fit_transform(X), y['section'].tolist())
#learner = input("Option? ")
learner = True
if learner:
# SVM
from sklearn.svm import SVC
C_range = np.linspace(1, 10, 100)
gamma_range = np.linspace(3000, 4000, 100)
param_dist = dict(gamma=gamma_range, C=C_range)
cv = StratifiedShuffleSplit(n_splits=5, test_size=0.3, random_state=42)
rnds = EvolutionaryAlgorithmSearchCV(estimator=SVC(max_iter=200),
params=param_dist,
scoring="f1",
cv=cv,
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=6,
n_jobs=4)
rnds.fit(X_train_little, y_train_little)
# summarize the results of the random parameter search
print(rnds.best_score_)
print('\nC: ')
print(rnds.best_estimator_.C)
print('\ngamma: ')
print(rnds.best_estimator_.gamma)
# apply best parameters
svc = SVC(max_iter=400,
C=rnds.best_estimator_.C,
for index, row in data.iterrows():#iterate over csv file
if index==limit:
break
img=cv2.imread(root_path + '/images/' + str(row['image_id']) + '.jpg')
histogram=np.zeros((3, 256))
for i in range(3):#calc hist for each channel
histogram[i] = cv2.calcHist([img],[i],None,[256],[0,255]).ravel()
X[index]=histogram.ravel()#to 1d array
return X
X = extract_features(train_data, 1500)
y = train_data['image_label'].values[:X.shape[0]].ravel()
grid = {
'knn__n_neighbors': [1, 10, 20, 30, 40, 60, 75, 100, 120, 160, 200],
'knn__metric': ['euclidean', 'manhattan', 'chebyshev'],
'knn__weights': ['uniform', 'distance'],
'preprocess__norm': ['l1', 'l2', 'max']
}
pipeline = Pipeline(steps=[
('preprocess', preprocessing.Normalizer()),
('knn', neighbors.KNeighborsClassifier())
])
model = EvolutionaryAlgorithmSearchCV(pipeline, grid, scoring='roc_auc', verbose=True, n_jobs=4, population_size=10)
model.fit(X, y)
preds = model.predict_proba(extract_features(test_data))[:, 1]
test_data = test_data.drop('image_url', 1)
test_data['image_label'] = preds
test_data.to_csv(root_path + '/res.csv', index=False)
#######################################################################
paramgrid = {"kernel":["poly"],
"C":C_range,
"gamma":gamma_range,
"degree":[3]
}
random.seed(1)
cv = EvolutionaryAlgorithmSearchCV(estimator=SVC(),
params=paramgrid,
scoring="accuracy",
cv=StratifiedKFold(trainlabel, n_folds=10),
verbose=True,
population_size=50,
gene_mutation_prob=0.10,
tournament_size=10,
generations_number=5,
n_jobs = 5)
cv.fit(train, trainlabel)
print cv.best_score_, cv.best_params_
##############################################
#ff = mysvc.training_manCV()
#ff.train_gene(train, trainlabel, 'poly', Cmin=-10, Cmax=10, numC=21, rmin=-10, rmax=10, numr=21, degree = 3)
pass
def score(self, Xt, yt):
return roc_auc_score(yt, outdet.IsolationForest(Xt, contamination=0.1, behaviour='new', return_scores = True, **self.params))
params = {
'max_samples': list(range(100, 1000)),
'n_estimators': list(range(50,200)),
'max_features': list(range(1, data.shape[1])) }
cv = EvolutionaryAlgorithmSearchCV (
estimator = odIfEstimator(),
params = params,
gene_type = [2, 2, 2],
verbose = 1,
population_size = 80,
gene_mutation_prob = .1,
gene_crossover_prob = .5,
tournament_size = 3,
generations_number = 8,
# this is already validation set, no need for cross validation
cv = ShuffleSplit(test_size=0.99, n_splits=1),
n_jobs = 40)
cv.fit(data, labels)
params = {
'k': list(range(100,1000)),
'x': list(range(3,30)),
'qv': [0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5] }
# use a full grid over all parameters
# run grid search
grid_search = GridSearchCV(clf, param_grid=param_grid)
start = time()
grid_search.fit(X, y)
print("GridSearchCV took %.2f seconds for %d candidate parameter settings." %
(time() - start, len(grid_search.cv_results_['params'])))
#report(grid_search.cv_results_)
print(grid_search.best_score_)
# run evolutionary_
evolution_search = EvolutionaryAlgorithmSearchCV(
estimator=clf,
params=param_grid,
#scoring="accuracy",
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=4,
)
start = time()
evolution_search.fit(X, y)
print("evolution_searchCV took %.2f seconds for %d candidates"
" parameter settings." % ((time() - start), n_iter_search))
print(evolution_search.cv_results_)
# pip install sklearn-deap
from evolutionary_search import EvolutionaryAlgorithmSearchCV
parameters = {'xg__learning_rate': [0.03, 0.05],
'xg__n_estimators': [200, 300],
'xg__max_depth': [4,6],
'pca__n_components' : [25,30]}
clf2 = EvolutionaryAlgorithmSearchCV(
estimator=pipe_xg, # How will objective be evaluated
params=parameters, # Parameters range
scoring="accuracy", # Criteria
cv=2, # No of folds
verbose=True,
population_size=50,
gene_mutation_prob=0.10,
tournament_size=3,
generations_number=10
)
start = time.time()
clf2.fit(X_train, y_train) # 1hr 2 minute
end = time.time()
(end-start)/60
clf2.best_params_
gene_crossover_prob=0.25,
tournament_size=2,
generations_number=3,
n_jobs=2)
#print(model.wv.most_similar('sensitive'))
cv.fit(tfidf_transformer.fit_transform(X), y['section'].tolist())
#cv.fit(MeanEmbeddingVectorizer(w2v).transform(X), y['section'].tolist())
'''
#--------------------------------- GA-SVC ---------------------------------
paramgrid = {"C": np.logspace(-9, 9, num=25, base=10)}
cv = EvolutionaryAlgorithmSearchCV(estimator=LinearSVC(),
params=paramgrid,
scoring="accuracy",
cv=StratifiedKFold(n_splits=4),
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=5,
n_jobs=4)
cv.fit(tfidf_transformer.fit_transform(X), y['section'].tolist())
class Wrap:
"""use GridSearchCV, RandomizedSearchCV and Evolutionary Search with this class.
Methods
-------
grid_method(self)
uses the GridSearchCV object and .fit()
:returns grid_history = grid.fit()
random_method(self)
uses the RandomizedSearchCV object and .fit()
:returns rand_history = rand.fit()
Attributes
----------
data_obj : (DataManager)
passed in object from DataManager class
network_obj: (NeuralArch)
architecture of the neural network, so we can use it in KerasRegressor
keras_regressor: (KerasRegressor)
a KerasRegressor object with build_fn=network_obj.build_nn"""
def __init__(self, data_obj, patience=10):
# todo explain why do we have these different object in the class
self.data_obj = data_obj
network_obj = Net(self.data_obj)
self.keras_regressor = KerasRegressor(build_fn=network_obj.build_nn)
self.callback = MyCallbacks(patience=patience)
def grid_method(self):
"""grid_method(self)
uses the GridSearchCV object and .fit()
:returns grid_history = grid.fit()"""
params = dict(epochs=[200], batch_size=[4, 8])
cv = [(slice(None), slice(None))
] # why have i written this over and over??
es = self.callback.es
mc = self.callback.mc
tb = self.callback.tb
csv_log = self.callback.csv_log
my_callbacks = [es, mc, csv_log]
self.grid = GridSearchCV(estimator=self.keras_regressor,
param_grid=params,
cv=cv)
grid_history = self.grid.fit(
X=self.data_obj.x_train,
y=self.data_obj.y_train,
validation_data=(self.data_obj.x_validation,
self.data_obj.y_validation),
verbose=0,
callbacks=my_callbacks)
return grid_history
def random_method(self):
"""grid_method(self)
uses the GridSearchCV object and .fit()
returns grid_history = grid.fit()"""
params = dict(epochs=[100],
batch_size=[2, 4, 8, 12, 16, 20, 24, 32, 36])
cv = [(slice(None), slice(None))]
es = self.callback.es
mc = self.callback.mc
tb = self.callback.tb
my_callbacks = [es, mc]
self.rand = RandomizedSearchCV(estimator=self.keras_regressor,
param_distributions=params,
n_iter=8)
rand_history = self.rand.fit(
X=self.data_obj.x_train,
y=self.data_obj.y_train,
validation_data=(self.data_obj.x_validation,
self.data_obj.y_validation),
verbose=0,
callbacks=my_callbacks)
return rand_history
def evolution_method(self):
# this does not work, but we need to continue
params = dict(epochs=[200], batch_size=[4, 8])
# cv = [(slice(None), slice(None))]
es = self.callback.es
mc = self.callback.mc
tb = self.callback.tb
my_callbacks = [es, mc]
fit_params = {
"epochs":
300,
"validation_data":
(self.data_obj.x_validation, self.data_obj.y_validation),
"callbacks":
my_callbacks
}
self.evo = EvolutionaryAlgorithmSearchCV(
estimator=self.keras_regressor,
params=params,
verbose=0,
population_size=10,
fit_params=fit_params)
evo_hist = self.evo.fit(X=self.data_obj.x_train,
y=self.data_obj.y_train)
return evo_hist
sizes=['10','50','100','150','200','250']
methods=['MRMR','JMI','JMIM']
targets=np.array(joblib.load('DatasetA_ValidationClasses.joblib.pkl'))
for method in methods:
for size in sizes:
random.seed(1)
X=X_original
indices= joblib.load(method+' PICKLES/selected_indices_'+method+'.joblib.pkl')
X=np.array(X)[:,indices]
indices= joblib.load(method+' PICKLES/'+size+'-'+method+'.joblib.pkl')
X=np.array(X)[:,indices]
f=open('genetic/'+method+'-'+size+'.txt','w')
print size
print method
print "svm.SVC"
f.write("svm.SVC\n")
cv = EvolutionaryAlgorithmSearchCV(estimator=SVC(),
params=paramgrid,
scoring="accuracy",
cv=StratifiedKFold(targets, n_folds=10),
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=5,
n_jobs=-1)
cv.fit(X, targets)
f.write('\n=======================\n')
def NN(X_train_little,
y_train_little,
X_train_pca,
X_test_pca,
y_train,
y_test,
tune_only=False):
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import cross_validate
num_features = len(X_train_little[0])
# prepare parameter grid
alpha_range = np.linspace(0.005, 0.015, 50)
learning_rate_range = np.linspace(0.01, 0.07, 50)
epsilon_range = np.logspace(-9, -6, 50)
beta_1_range = np.linspace(0.3, 0.7, 50)
beta_2_range = np.linspace(0.3, 0.7, 50)
a = int((num_features + 1) / 2)
b = int((num_features + 1) / 2 + 10)
med_layer_range = np.arange(a, b)
param_dist = dict(alpha=alpha_range,
hidden_layer_sizes=(num_features, med_layer_range, 1),
learning_rate_init=learning_rate_range,
epsilon=epsilon_range,
beta_1=beta_1_range,
beta_2=beta_2_range)
cv = StratifiedShuffleSplit(n_splits=5, test_size=0.2, random_state=SEED)
rnds = EvolutionaryAlgorithmSearchCV(
estimator=MLPClassifier(early_stopping=True),
params=param_dist,
scoring="f1",
cv=cv,
verbose=1,
population_size=50,
gene_mutation_prob=0.10,
gene_crossover_prob=0.5,
tournament_size=3,
generations_number=6,
n_jobs=4)
rnds.fit(X_train_little, y_train_little)
# summarize the results of the random parameter search
file = open("MLP_best_parameters.txt", "w")
file.write("{}\n".format(rnds.best_score_))
file.write('alpha: {}\n'.format(rnds.best_estimator_.alpha))
file.write('hidden_layer_sizes: {}\n'.format(
rnds.best_estimator_.hidden_layer_sizes))
file.write('learning_rate_init: {}\n'.format(
rnds.best_estimator_.learning_rate_init))
file.write('epsilon: {}\n'.format(rnds.best_estimator_.epsilon))
file.write('beta_1: {}\n'.format(rnds.best_estimator_.beta_1))
file.write('beta_2: {}\n'.format(rnds.best_estimator_.beta_2))
file.close()
if not tune_only:
# apply best parameters
mlp = MLPClassifier(
hidden_layer_sizes=rnds.best_estimator_.hidden_layer_sizes,
early_stopping=True,
alpha=rnds.best_estimator_.alpha,
learning_rate_init=rnds.best_estimator_.learning_rate_init,
epsilon=rnds.best_estimator_.epsilon,
beta_1=rnds.best_estimator_.beta_1,
beta_2=rnds.best_estimator_.beta_2,
random_state=SEED)
mlp.fit(X_train_pca, y_train)
sc_tr = cross_validate(mlp,
X_train_pca,
y_train,
scoring=SCORING,
cv=5,
return_train_score=False)
sc_ts = cross_validate(mlp,
X_test_pca,
y_test,
scoring=SCORING,
cv=5,
return_train_score=False)
pred = mlp.predict(X_test_pca)
pred_train = mlp.predict(X_train_pca)
output_report("MLP", y_train, pred_train, y_test, pred, sc_tr, sc_ts)
