def final_feats(df_data):
x_train = df_data.iloc[:,1:370] #removing the "ID" and the "Target" columns
"""Getting the first 2 PCs"""
pca = PCA(n_components=2)
x_train_projected = pca.fit_transform(normalize(x_train, axis=0))
x_train, del_constants = remove_feat_constants(x_train)
""" removing columns with no
variance; in our case the all-zero columns"""
x_train, del_identicals = remove_feat_identicals(x_train)
"""removing columns that are identical to each other, and retainining
only one of them"""
y_train = df_data["TARGET"]
# Using L1 based feature selection on X_train with 308 columns
lsvc = svm.LinearSVC(C=0.01, penalty="l1", dual=False).fit(x_train, y_train)
model = SelectFromModel(lsvc, prefit=True)
feat_ix_keep = model.get_support(indices=True) #getting indices of selected features
#so that I don't have to use "transform" and convert the data frame to a matrix.
orig_feat_ix = np.arange(x_train.columns.size)
feat_ix_delete = np.delete(orig_feat_ix, feat_ix_keep)
X_train_new = x_train.drop(labels=x_train.columns[feat_ix_delete],
axis=1)
X_train_new.insert(1, 'PCAOne', x_train_projected[:, 0])
X_train_new.insert(1, 'PCATwo', x_train_projected[:, 1])
return X_train_new, y_train, feat_ix_keep, pca, del_constants, del_identicals
def selecttest():
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_boston
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LassoCV
boston = load_boston()
X,y = boston['data'], boston['target']
clf = LassoCV()
sfm = SelectFromModel(clf, threshold=0.25)
sfm.fit(X,y)
n_features = sfm.transform(X).shape[1]
while n_features > 2:
sfm.threshold += 0.1
X_transform = sfm.transform(X)
n_features = X_transform.shape[1]
plt.title(
"Features selected from Boston using SelectFromModel with "
"threshold %0.3f." % sfm.threshold)
feature1 = X_transform[:, 0]
feature2 = X_transform[:, 1]
plt.plot(feature1, feature2, 'r.')
plt.xlabel("Feature number 1")
plt.ylabel("Feature number 2")
plt.ylim([np.min(feature2), np.max(feature2)])
plt.show()
def tree_based_selection(self, data_set, data_target, feature_names):
"""
:param data_set:
:return:
"""
clf = ExtraTreesClassifier()
clf = clf.fit(data_set, data_target)
print clf.feature_importances_
model = SelectFromModel(clf, prefit=True)
feature_set = model.transform(data_set)
fea_index = []
for A_col in np.arange(data_set.shape[1]):
for B_col in np.arange(feature_set.shape[1]):
if (data_set[:, A_col] == feature_set[:, B_col]).all():
fea_index.append(A_col)
check = {}
for i in fea_index:
check[feature_names[i]] = data_set[0][i]
print np.array(check)
return feature_set, fea_index
def select_features(data, neg_cmpd, pos_cmpd, compound_col="Metadata_compound",
C=0.01):
"""
Return selected features basd on L1 linear svc.
Parameters
-----------
data : pandas DataFrame
neg_cmpd : string
name of negative control in compound_col
pos_cmpd : string
name of positive control in compound_col
compound_col : string
name of column in data that contains compound labels
C : float (default=0.01)
Sparsity, lower the number the fewer features are selected
Returns
-------
selected_features : list
Selected features
"""
X, Y = _split_classes(data, neg_cmpd, pos_cmpd, compound_col)
lin_svc = LinearSVC(C=C, penalty="l1", dual=False).fit(X, Y)
model = SelectFromModel(lin_svc, prefit=True)
feature_mask = np.array(model.get_support())
feature_names = np.array(X.columns.tolist())
selected_features = list(feature_names[feature_mask])
return selected_features
def lassoCV_regression(data,target,alphas):
clf=LassoCV()
sfm = SelectFromModel(clf, threshold=0.25)
sfm.fit(data, target)
n_features = sfm.transform(data).shape[1]
while n_features > 2:
sfm.threshold += 0.1
data_transform = sfm.transform(data)
n_features = data_transform.shape[1]
rmses=[]
kf=KFold(len(target),10,True,None)
for train_index, test_index in kf:
data_train,data_test=data_transform[train_index],data_transform[test_index]
target_train,target_test=target[train_index],target[test_index]
clf.fit(data_train,target_train)
rmse=sqrt(np.mean((clf.predict(data_test)-target_test)**2))
rmses.append(rmse)
x0=np.arange(1,11)
plt.figure()
plt.plot(x0,rmses,label='LassoCV')
plt.legend()
plt.show()
return rmses
def run():
allKeys, X, y = loadData("../../data/household_electricity_usage/recs2009_public.csv", label = "BTUEL", otherRemove =
["KWH", "KWHSPH", "KWHCOL", "KWHWTH", "KWHRFG", "KWHOTH", "BTUEL", "BTUELSPH", "BTUELCOL", "BTUELWTH", "BTUELRFG","BTUELOTH",
"DOLLAREL", "DOLELSPH", "DOLELCOL", "DOLELWTH", "DOLELRFG", "DOLELOTH", "TOTALBTUOTH", "TOTALBTUCOL", 'TOTALBTU', 'TOTALBTUWTH',
'TOTALBTU', 'TOTALBTUSPH', 'TOTALBTURFG', 'TOTALDOL', 'TOTALDOLSPH', 'TOTALDOLCOL', 'TOTALDOLWTH', 'TOTALDOLRFG', 'TOTALDOLOTH'])
#allKeys, X, y = loadData("../../data/household_electricity_usage/recs2009_public.csv", label = "BTUEL", otherRemove = [], forceUse =
# [
# 'WGTP', 'NP', 'TYPE', 'ACR', 'BDSP', 'BATH', 'FS','MHP', 'RMSP', 'RNTP', 'REFR', 'RNTP', 'RWAT', 'STOV', 'TEN', 'VALP', 'YBL', 'FES', 'FINCP', 'HINCP', 'HHT', 'KIT', 'NOC', 'NPF', 'PLM', 'SRNT', 'SVAL', 'TAXP', 'WIF', 'WORKSTAT',
# ])
clf = RandomForestRegressor(n_estimators = 100, n_jobs = 7)
clf.fit(X, y)
model = SelectFromModel(clf, prefit = True)
X = model.transform(X)
relevantFeatures = [allKeys[i] for i in range(len(model._get_support_mask())) if model._get_support_mask()[i] == True]
print("Relevant Features", relevantFeatures)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.25)
clf.fit(X_train, y_train)
print(y_test[:100])
print(metrics.mean_squared_error(clf.predict(X_test), y_test))
features = sorted(zip(allKeys, clf.feature_importances_), key = lambda x : x[1], reverse = True)
print("Features", features)
def select_features(x, y, methods=('variance', 'correlation', 'l1', 'forest')):
''' methods = ('variance', 'correlation', 'l1', 'forest')
- variance: use variance threshold to discard features that are mostly 0 or 1
- correlation: use chi2 test to remove most very correlated features
- l1: use l1 penalty to remove features that make solution sparse
- forest: use ExtraTreesClassifier to point out importance of features
select important ones
'''
features = x.loc[:,'Feature_1':'Feature_2']
if 'variance' in methods:
vt = VT(threshold=(0.99*(1-0.99)))
vt.fit(features)
if 'correlation' in methods:
cr = SP(f_regression, percentile=80)
if 'l1' in methods:
rgr = MultiTaskLassoCV(cv=5, n_jobs=-1)
m = SFM(rgr)
if 'forest' in methods:
clf = RandomRorestRegressor(n_estimators=300, max_features=0.7,n_jobs=-1).fit(x,y)
m = SFM(clf)
m.fit(x.values, y.values)
for indices in idx_list:
x_indices = x_indices & indices
print 'All: %s' % len(x_indices)
return list(x_indices)
def feature_selction(_train_data, _valid_data, _test_data, _train_label, _valid_label, _test_label):
train_imageNo = _train_data.shape[0]
valid_imageNo = _valid_data.shape[0]
whole_data = numpy.concatenate((_train_data, _valid_data, _test_data))
whole_data = whole_data.reshape((-1, 120))
whole_label = numpy.concatenate((_train_label, _valid_label, _test_label))
whole_label = list(whole_label)
new_label_list = list()
for i in whole_label:
for j in range(100):
new_label_list.append(i)
assert len(new_label_list) == whole_data.shape[0]
lsvc = LinearSVC(C=0.1, penalty="l1", dual=False).fit(whole_data, new_label_list)
model = SelectFromModel(lsvc, prefit=True)
data_new = model.transform(whole_data)
print ('After feature selection we have', data_new.shape[1], 'features.')
data_new = data_new.reshape((-1, 100, data_new.shape[1]))
_train_data = data_new[:train_imageNo,:,:]
_valid_data = data_new[train_imageNo:train_imageNo+valid_imageNo,:,:]
_test_data = data_new[train_imageNo+valid_imageNo:,:,:]
return _train_data, _valid_data, _test_data
def feature_importance_with_forest(rforest_classifier, issues_train, priority_train, issues_test, priority_test):
"""
Assess feature importance using a Random Forest.
:param rforest_classifier: An already fitted classifier.
:param issues_train: Train features.
:param priority_train: Train classes.
:param issues_test: Test features.
:param priority_test: Test classes.
:return: None
"""
importances = rforest_classifier.feature_importances_
indices = np.argsort(importances)[::-1]
for column_index in range(len(issues_train.columns)):
print column_index + 1, ") ", issues_train.columns[column_index], " ", importances[indices[column_index]]
figure, axes = plt.subplots(1, 1)
plt.title('Feature importance')
plt.bar(range(len(issues_train.columns)), importances[indices], color='lightblue', align='center')
plt.xticks(range(len(issues_train.columns)), issues_train.columns, rotation=90)
plt.xlim([-1, len(issues_train.columns)])
plt.tight_layout()
plt.show()
evaluate_performance("FOREST", rforest_classifier, issues_train, priority_train, issues_test, priority_test)
print "Selecting important features ..."
select = SelectFromModel(rforest_classifier, threshold=0.05, prefit=True)
train_selected = select.transform(issues_train)
test_selected = select.transform(issues_test)
rforest_classifier.fit(train_selected, priority_train)
evaluate_performance("FOREST-IMPORTANT", rforest_classifier, train_selected, priority_train, test_selected,
priority_test)
def forests(input_df, target_df): """This method implements two types of forest features selection. ExtraTreesClassifier & RandomForestClassifier. Features are ranked in order of importance.""" from sklearn.ensemble import ExtraTreesClassifier from sklearn.feature_selection import SelectFromModel clf = ExtraTreesClassifier(random_state = 0) clf = clf.fit(input_df, target_df) model = SelectFromModel(clf, prefit=True) input_df_new = model.transform(input_df) original_space = input_df.shape new_space_ETC = input_df_new.shape tuple_holder = [(j, i) for i, j in zip(feature_space, clf.feature_importances_)] tuple_holder.sort() tuple_holder.reverse() ################################################ ################################################ from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier(random_state = 0) clf = clf.fit(input_df, target_df) model = SelectFromModel(clf, prefit=True) input_df_new = model.transform(input_df) new_space_RFC = input_df_new.shape tuple_holder_2 = [(j, i) for i, j in zip(feature_space, clf.feature_importances_)] tuple_holder_2.sort() tuple_holder_2.reverse() ################################################ ################################################ rank_number = 0 print 'ExtraTreesClassifier', '\t'*4, 'RandomForestClassifier' print 'Old Space: ', original_space, '\t'*4, 'Old Space:', original_space print 'New Space: ', new_space_ETC, '\t'*4, 'New Space:', new_space_RFC for i, j in zip(tuple_holder, tuple_holder_2): rank_number += 1 print rank_number, '|', i, '\t'*3, rank_number, '|', j
def test_max_features_dim(max_features):
clf = RandomForestClassifier(n_estimators=50, random_state=0)
transformer = SelectFromModel(estimator=clf,
max_features=max_features,
threshold=-np.inf)
X_trans = transformer.fit_transform(data, y)
assert X_trans.shape[1] == max_features
def test_invalid_input():
clf = SGDClassifier(alpha=0.1, max_iter=10, shuffle=True,
random_state=None, tol=None)
for threshold in ["gobbledigook", ".5 * gobbledigook"]:
model = SelectFromModel(clf, threshold=threshold)
model.fit(data, y)
assert_raises(ValueError, model.transform, data)
def lasso_reducer(X, y):
clf = LassoCV()
# Set a minimum threshold of 0.25
# this is a 'maxing out' of the sum of all coefficients
sfm = SelectFromModel(clf, threshold=0.25)
sfm.fit(X, y)
n_features = sfm.transform(X).shape[1]
# reset the threshold until the number of features equals two.
# Note that the attribute can be set directly instead of repeatedley
# fitting the metatransformer.
while n_features > 2:
sfm.threshold += 0.1
X_transform = sfm.transform(X)
n_features = X_transform.shape[1]
# Plot the seelcted two features from X.
plt.title('features selected from boston using the SelectFromModel with'
'threshold of %0.3f.' % sfm.threshold)
feature1 = X_transform[:, 0]
feature2 = X_transform[:, 1]
plt.plot(feature1, feature2, 'r.')
plt.xlabel("Value of Feature number 1")
plt.ylabel("Value of Feature number 2")
plt.ylim([np.min(feature2), np.max(feature2)])
plt.show()
return
def test_feature_importances_2d_coef():
X, y = datasets.make_classification(
n_samples=1000,
n_features=10,
n_informative=3,
n_redundant=0,
n_repeated=0,
shuffle=False,
random_state=0,
n_classes=4,
)
est = LogisticRegression()
for threshold, func in zip(["mean", "median"], [np.mean, np.median]):
for order in [1, 2, np.inf]:
# Fit SelectFromModel a multi-class problem
transformer = SelectFromModel(estimator=LogisticRegression(), threshold=threshold, norm_order=order)
transformer.fit(X, y)
assert_true(hasattr(transformer.estimator_, "coef_"))
X_new = transformer.transform(X)
assert_less(X_new.shape[1], X.shape[1])
# Manually check that the norm is correctly performed
est.fit(X, y)
importances = norm(est.coef_, axis=0, ord=order)
feature_mask = importances > func(importances)
assert_array_equal(X_new, X[:, feature_mask])
class SelectFromModelSelection(SelectionModel):
name = "SelectFromModel"
def __init__(self, *args):
SelectionModel.__init__(self, *args)
self.selector = SelectFromModel(self.estimator)
self.selector.fit(self.x_array, self.y_array)
self.support_ = self.selector.get_support()
def test_warm_start():
est = PassiveAggressiveClassifier(warm_start=True, random_state=0)
transformer = SelectFromModel(estimator=est)
transformer.fit(data, y)
old_model = transformer.estimator_
transformer.fit(data, y)
new_model = transformer.estimator_
assert_true(old_model is new_model)
def rf_feat_reduction(rf_model, features):
print " Reducing number of input features based on feature importance."
subset_model = SelectFromModel(rf_model, prefit=True)
feat_subset = subset_model.transform(features)
feat_bool = subset_model.get_support()
print " " + str(len(feat_subset[0])) + " features chosen after model selection."
return feat_subset, feat_bool
def test_max_features_error(max_features, err_type, err_msg):
clf = RandomForestClassifier(n_estimators=50, random_state=0)
transformer = SelectFromModel(estimator=clf,
max_features=max_features,
threshold=-np.inf)
with pytest.raises(err_type, match=err_msg):
transformer.fit(data, y)
def test_input_estimator_unchanged():
"""
Test that SelectFromModel fits on a clone of the estimator.
"""
est = RandomForestClassifier()
transformer = SelectFromModel(estimator=est)
transformer.fit(data, y)
assert_true(transformer.estimator is est)
def selectfeature(x, y, x_pre): x, x_pre = datscater(x, x_pre) clf = linear_model.LassoLars().fit(x, y) model = SelectFromModel(clf, prefit=True) x_new = model.transform(x) print 'x',x.shape print x_new.shape x_pre = model.transform(x_pre) return x_new, x_pre
def lgb_feature_selection(fe_name, matrix_x_temp, label_y, th):
# SelectfromModel
clf = LGBMClassifier(n_estimators=400)
clf.fit(matrix_x_temp, label_y)
sfm = SelectFromModel(clf, prefit=True, threshold=th)
matrix_x = sfm.transform(matrix_x_temp)
# 打印出有多少特征重要性非零的特征
feature_score_dict = {}
for fn, s in zip(fe_name, clf.feature_importances_):
feature_score_dict[fn] = s
m = 0
for k in feature_score_dict:
if feature_score_dict[k] == 0.0:
m += 1
print 'number of not-zero features:' + str(len(feature_score_dict) - m)
# 打印出特征重要性
feature_score_dict_sorted = sorted(feature_score_dict.items(),
key=lambda d: d[1], reverse=True)
print 'feature_importance:'
for ii in range(len(feature_score_dict_sorted)):
print feature_score_dict_sorted[ii][0], feature_score_dict_sorted[ii][1]
print '\n'
f = open('../eda/lgb_feature_importance.txt', 'w')
f.write(th)
f.write('\nRank\tFeature Name\tFeature Importance\n')
for i in range(len(feature_score_dict_sorted)):
f.write(str(i) + '\t' + str(feature_score_dict_sorted[i][0]) + '\t' + str(feature_score_dict_sorted[i][1]) + '\n')
f.close()
# 打印具体使用了哪些字段
how_long = matrix_x.shape[1] # matrix_x 是 特征选择后的 输入矩阵
feature_used_dict_temp = feature_score_dict_sorted[:how_long]
feature_used_name = []
for ii in range(len(feature_used_dict_temp)):
feature_used_name.append(feature_used_dict_temp[ii][0])
print 'feature_chooesed:'
for ii in range(len(feature_used_name)):
print feature_used_name[ii]
print '\n'
f = open('../eda/lgb_feature_chose.txt', 'w')
f.write('Feature Chose Name :\n')
for i in range(len(feature_used_name)):
f.write(str(feature_used_name[i]) + '\n')
f.close()
# 找到未被使用的字段名
feature_not_used_name = []
for i in range(len(fe_name)):
if fe_name[i] not in feature_used_name:
feature_not_used_name.append(fe_name[i])
return matrix_x, feature_not_used_name[:], len(feature_used_name)
def select_feature(clf,x_train,x_valid):
clf.fit(x_train, y_train)
model = SelectFromModel(clf, prefit=True, threshold="mean")
print x_train.shape
x_train = model.transform(x_train)
x_valid = model.transform(x_valid)
print x_train.shape
return x_train,x_valid
def train(self):
rfc = RandomForestRegressor()
rfc.fit(self.data, self.target)
model = SelectFromModel(rfc, prefit=True)
X = model.transform(self.data)
self.predict = model.transform(self.predict)
rfc.fit(X, self.target)
return rfc
def select_features_tree(X, y, feature_names = []):
print X.shape
#forest = RandomForestClassifier(n_estimators=1000, n_jobs=4)
forest = ExtraTreesClassifier(n_estimators=1000, n_jobs=8)
fo = forest.fit(X, y)
sorted_feature_names = plot_feature_importance(fo, X, feature_names)
model = SelectFromModel(fo, prefit=True, )
X_new = model.transform(X)
print X_new.shape
return X_new, sorted_feature_names[0:X_new.shape[1]]
def test_coef_default_threshold():
X, y = datasets.make_classification(
n_samples=100, n_features=10, n_informative=3, n_redundant=0,
n_repeated=0, shuffle=False, random_state=0)
# For the Lasso and related models, the threshold defaults to 1e-5
transformer = SelectFromModel(estimator=Lasso(alpha=0.1))
transformer.fit(X, y)
X_new = transformer.transform(X)
mask = np.abs(transformer.estimator_.coef_) > 1e-5
assert_array_almost_equal(X_new, X[:, mask])
def test_threshold_string():
est = RandomForestClassifier(n_estimators=50, random_state=0)
model = SelectFromModel(est, threshold="0.5*mean")
model.fit(data, y)
X_transform = model.transform(data)
# Calculate the threshold from the estimator directly.
est.fit(data, y)
threshold = 0.5 * np.mean(est.feature_importances_)
mask = est.feature_importances_ > threshold
assert_array_equal(X_transform, data[:, mask])
def select_features(inputs, label, threshold):
print 'training ExtraTreesClassifier...'
clf = ExtraTreesClassifier(criterion='entropy')
clf.fit(inputs, label)
threshold='%f*mean'%(threshold)
print 'training SelectFromModel, threshold=%s...'%(threshold)
sfm = SelectFromModel(clf, threshold=threshold, prefit=True)
inputs_new = sfm.transform(inputs)
#pdb.set_trace()
print inputs_new.shape
return sfm, inputs_new
def extra_trees_classifier():
tianic=Titanic_Data('../input/train.csv','../input/test.csv')
combined_normalized_data=tianic.get_normalized_data()
train,test,targets = recover_train_test_target('../input/train.csv', combined_normalized_data)
clf = ExtraTreesClassifier(n_estimators=200)
clf = clf.fit(train, targets)
features = pd.DataFrame()
features['feature'] = train.columns
features['importance'] = clf.feature_importances_
features.sort(['importance'],ascending=False)
model = SelectFromModel(clf, prefit=True)
train_new = model.transform(train)
train_new.shape
test_new = model.transform(test)
test_new.shape
forest = RandomForestClassifier(max_features='sqrt')
parameter_grid = {
'max_depth' : [4,5,6,7,8],
'n_estimators': [200,210,240,250],
'criterion': ['gini','entropy']
}
cross_validation = StratifiedKFold(targets, n_folds=5)
grid_search = GridSearchCV(forest,
param_grid=parameter_grid,
cv=cross_validation)
grid_search.fit(train_new, targets)
print('Best score: {}'.format(grid_search.best_score_))
print('Best parameters: {}'.format(grid_search.best_params_))
output = grid_search.predict(test_new).astype(int)
df_output = pd.DataFrame()
df_output['PassengerId'] = test['PassengerId']
df_output['Survived'] = output
df_output[['PassengerId','Survived']].to_csv('./extra_trees_classifier_output.csv',index=False)
def test_partial_fit():
est = PassiveAggressiveClassifier(random_state=0, shuffle=False)
transformer = SelectFromModel(estimator=est)
transformer.partial_fit(data, y, classes=np.unique(y))
old_model = transformer.estimator_
transformer.partial_fit(data, y, classes=np.unique(y))
new_model = transformer.estimator_
assert_true(old_model is new_model)
X_transform = transformer.transform(data)
transformer.fit(np.vstack((data, data)), np.concatenate((y, y)))
assert_array_equal(X_transform, transformer.transform(data))
def execute(fdata):
data = list()
target = list()
storeDict = dict()
for i, lines in enumerate(fdata):
sline = lines.split(",")
target.append(int(sline[0]))
data.append([float(x) for j, x in enumerate(sline) if j != 0])
storeDict[i] = [float(x) for j, x in enumerate(sline) if j != 0]
data = np.array(data)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(data, target, test_size=0.25, random_state=0)
clf = ExtraTreesClassifier()
clf = clf.fit(X_train, y_train)
model = SelectFromModel(clf, prefit=True)
X_new = model.transform(X_train)
clfNew = svm.SVC(kernel='linear', C=1).fit(X_new, y_train)
value_feature = list()
countDict = dict()
for key, val in storeDict.items():
countDict[key] = 0
for i, inval in enumerate(val):
if inval in X_new[0]:
countDict[key] = countDict[key] + 1
keyName = max(countDict, key=countDict.get)
posStore = list()
for val in X_new[0]:
posStore.append(storeDict[keyName].index(val))
X_test_new = list()
for val in X_test:
inlist = list()
for i, inval in enumerate(val):
if i in posStore:
inlist.append(inval)
X_test_new.append(inlist)
X_test_new = np.array(X_test_new)
return accuracy_score(y_test, clf.predict(X_test)), accuracy_score(y_test, clfNew.predict(X_test_new))
model_filter = SelectKBest(f_classif, k=10)
lr = LogisticRegression(max_iter=100, class_weight=None)
model_pl = Pipeline([('SelectKBest', model_filter),
('LogisticRegression', lr)])
model_pl.fit(x_train, y_train)
model_pl.predict(x_test)
print model_pl.score(x_test, y_test)
print model_pl.named_steps['SelectKBest'].get_support()
print dataset.atribNombre([
i for i, x in enumerate(
model_pl.named_steps['SelectKBest'].get_support().tolist()) if x
])
lr = LogisticRegression(max_iter=100, class_weight=None)
lrS = LogisticRegression(max_iter=100,
class_weight=None).fit(x_train, y_train)
model_filter = SelectFromModel(lrS)
print '\nSelectFromModel'
model_pl = Pipeline([('SelectFromModel', model_filter),
('LogisticRegression', lr)])
model_pl.fit(x_train, y_train)
model_pl.predict(x_test)
print model_pl.score(x_test, y_test)
print model_pl.named_steps['SelectFromModel'].get_support()
print dataset.atribNombre([
i for i, x in enumerate(
model_pl.named_steps['SelectFromModel'].get_support().tolist())
if x
])
def logistic_dimension(data, label, parameter=1):
logistic_ = LogisticRegression(penalty="l1", C=parameter, max_iter=30)
model = SelectFromModel(logistic_)
new_data = model.fit_transform(data, label)
mask = model.get_support(indices=True)
return new_data, mask
fraudInstanceData = pd.read_csv("FraudInstanceData.csv", header=0, index_col=0)
maritalStatuses = pd.get_dummies(fraudInstanceData["Marital Status"])
accomodationTypes = pd.get_dummies(fraudInstanceData["Accomodation Type"])
fraudInstanceData = fraudInstanceData.drop('Marital Status', axis=1)
fraudInstanceData = fraudInstanceData.drop('Accomodation Type', axis=1)
fraudInstanceData = fraudInstanceData.join(maritalStatuses)
fraudInstanceData = fraudInstanceData.join(accomodationTypes)
currencyToMoney = lambda c: Decimal(sub(r'[^\d.]', '', c))
fraudInstanceData['Claim Amount'] = fraudInstanceData["Claim Amount"].apply(
currencyToMoney)
y = fraudInstanceData.iloc[:, 1]
X = fraudInstanceData.iloc[:, 1:]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=23)
pipeline = Pipeline([('feature_selection',
SelectFromModel(LogisticRegression(penalty="l1"))),
('regression', LogisticRegression())])
grid_cv = GridSearchCV(pipeline, {}, cv=10)
grid_cv.fit(X_train, y_train)
selected_feature = grid_cv.transform(X_train.co)
y_pred = grid_cv.predict(X_test)
print(grid_cv.score(X_test, y_pred))
print(confusion_matrix(y_test, y_pred))
def build_model(self, fname_structlib):
#MODIFIED BY JIM (this way don't have to remember to close the file...)
with open(fname_structlib, 'rb') as f_structlib:
structs = pickle.load(f_structlib)
n_structs = 0
for struct in structs:
if not struct.metricpredicted:
n_structs += 1
metrics = np.zeros(n_structs)
n_features = 0
for prop in self.properties:
if prop.useful:
n_features += 1
features = np.zeros((n_structs, n_features))
count_structs = 0
for struct in structs:
if not struct.metricpredicted:
props = self.calc_properties(struct)
count_features = 0
for prop in self.properties: # make sure this happens in the same order each time
if prop.useful:
#Need to prune properties we don't need (i.e. smaller rdf, etc.)
try:
features[count_structs,
count_features] = props[prop.label]
count_features += 1
except KeyError:
#Remove this property so don't have to do this again
prop.useful = False
metrics[count_structs] = struct.metric
count_structs += 1
# cross-validation etc. etc. and change property.useful's
# need to make sure that property.useful status is consistent with the model (has same number of features)
# make new model to test with
test_model = clone(self.model)
test_scaler = clone(self.scaler)
# split data into testing and training sets
features_train, features_test, metrics_train, metrics_test = train_test_split(
features, metrics, test_size=0.25, shuffle=True)
# using training set, perform feature selection by selecting from fitted LASSO model
features_train_scaled = test_scaler.fit_transform(features_train)
features_test_scaled = test_scaler.transform(features_test)
selector = SelectFromModel(test_model,
threshold=1e-4) # HARD CODED NUMBER HERE
selector.fit(features_train_scaled, metrics_train)
print('number of features selected',
np.sum(selector.get_support().astype(int)))
features_train_reduced_unscaled = selector.transform(features_train)
features_test_reduced_unscaled = selector.transform(features_test)
# using training set, perform recursive feature elimination with cross-validation
# selector = RFECV(test_model, step=1, scoring='neg_mean_squared_error')
# features_train_new = selector.fit_transform(features_train, metrics_train)
# print('number of features selected after cross-validation', selector.n_features_)
# features_test_new = selector.transform(features_test)
# features_new = selector.transform(features)
# fit with reduced number of features
features_train_reduced_scaled = test_scaler.fit_transform(
features_train_reduced_unscaled)
features_test_reduced_scaled = test_scaler.transform(
features_test_reduced_unscaled)
test_model.fit(features_train_reduced_scaled, metrics_train)
# compute RMSE of test set
# should also compute for training set??
mse_test = mean_squared_error(
metrics_test, test_model.predict(features_test_reduced_scaled))
# Below switching to using coefficient of determination, not RMSE, but still calling it RMSE
# This normalizes things to the variance in the data, so now want to be bigger and close to 1
# A good cutoff is probably 0.8 or 0.9
#rmse_norm_new = np.sqrt(mse_test)/np.mean(metrics)
rmse_norm_new = (np.var(metrics) - mse_test) / np.var(metrics)
print('rmse_norm_new', rmse_norm_new)
print('self.rmse_norm', self.rmse_norm)
#if rmse_norm_new < self.rmse_norm: # should we do something fancier than this?
# copy model (or should we maybe refit it to all the data?? not sure if this would violate something machine learning)
self.scaler = clone(test_scaler)
features_train_reduced_scaled = self.scaler.fit_transform(
features_train_reduced_unscaled)
self.model = clone(test_model)
self.model.fit(features_train_reduced_scaled, metrics_train)
# change useful labels on properties
count_features = 0
selector_support = selector.get_support()
for prop in self.properties:
if prop.useful:
prop.useful = selector_support[count_features]
count_features += 1
if rmse_norm_new > self.rmse_norm: # should we do something fancier than this?
self.rmse_norm = rmse_norm_new
return True
else:
return False
forest.fit(X_train, y_train)
importances = forest.feature_importances_
indices = np.argsort(importances)[::-1] # Extented Slice -> reverse array
print('Features ranked:')
for f in range(X_train.shape[1]):
print(f'{f+1}) {feat_labels[indices[f]]:<30} {importances[indices[f]]}')
import matplotlib.pyplot as plt
plt.title('Feature Importance')
plt.bar(range(X_train.shape[1]), importances[indices], align='center')
plt.xticks(range(X_train.shape[1]), feat_labels[indices], rotation=90)
plt.xlim([-1, X_train.shape[1]])
plt.tight_layout()
plt.show()
# Note: highly correlated features may not all be ranked high
from sklearn.feature_selection import SelectFromModel
sfm = SelectFromModel(forest, threshold=0.1, prefit=True)
X_selected = sfm.transform(X_train)
print('Number of features that meet this threshold: {X_selected.shape[1]}')
for f in range(X_selected.shape[1]):
print(f'{f+1}) {feat_labels[indices[f]]:<30} {importances[indices[f]]:3f}')
dataframe['label'].value_counts()
x_matrix = dataframe.copy()
x_matrix.drop(['label'], axis=1, inplace=True)
y_vector = dataframe['label']
sc = StandardScaler()
sc.fit(x_matrix)
x_matrix = sc.transform(x_matrix)
############feature selection
classif = ExtraTreesClassifier(n_estimators=100)
classif = classif.fit(x_matrix, y_vector)
classif.feature_importances_
selected = SelectFromModel(classif, prefit=True)
x_matrix_new = selected.transform(x_matrix)
x_matrix_new.shape
X_train, X_test, y_train, y_test = train_test_split(x_matrix_new, y_vector, test_size=0.33, random_state=42,shuffle=True)
#############
classif_LR = LogisticRegression()
classif_KN = KNeighborsClassifier()
classif_RF = RandomForestClassifier()
###############Log
print("%2d) %-*s %f" % (f + 1, 30,
feat_labels[indices[f]],
importances[indices[f]]))
#feature selection threshold - deprecated
X.shape
X_selected = clf.transform(X, threshold=0.02)
X_selected.shape
#SelectModel method
from sklearn.svm import LinearSVC
from sklearn.feature_selection import SelectFromModel
X.shape
lsvc = LinearSVC(C=0.1, penalty="l1", dual=False).fit(X, y)
model = SelectFromModel(lsvc, prefit=True)
X_new = model.transform(X)
X_new.shape
yhat = clf.predict_proba(X) #which is which?
clf.classes_
yhat.shape
X.shape
yhat[:,0].shape
X0['yhats_A'] = yhat[:,0]
X0['yhats_D'] = yhat[:,1]
X0['yhats_H'] = yhat[:,2]
class LinearSVM(SemevalModel):
def __init__(self):
SemevalModel.__init__(self)
def __transform__(self, q1, q2):
if type(q1) == list: q1 = ' '.join(q1)
if type(q2) == list: q2 = ' '.join(q2)
lcs = features.lcs(re.split('(\W)', q1), re.split('(\W)', q2))
lcs1 = len(lcs[1].split())
lcs2 = lcs[0]
lcsub = features.lcsub(q1, q2)[0]
jaccard = features.jaccard(q1, q2)
containment_similarity = features.containment_similarities(q1, q2)
# greedy_tiling = features.greedy_string_tiling(q1, q2)
X = [lcs1, lcsub, jaccard, containment_similarity]
# ngram features
for n in range(2, 5):
ngram1 = ' '
for gram in nltk.ngrams(q1.split(), n):
ngram1 += 'x'.join(gram) + ' '
ngram2 = ' '
for gram in nltk.ngrams(q2.split(), n):
ngram2 += 'x'.join(gram) + ' '
lcs = features.lcs(re.split('(\W)', ngram1),
re.split('(\W)', ngram2))
X.append(len(lcs[1].split()))
# X.append(lcs[0])
X.append(features.lcsub(ngram1, ngram2)[0])
X.append(features.jaccard(ngram1, ngram2))
X.append(features.containment_similarities(ngram1, ngram2))
return X
def get_features(self, q1id, q1, q2id, q2, set='train'):
X = []
if set == 'train':
q1_elmo = self.trainelmo.get(str(self.trainidx[q1id]))
q2_elmo = self.trainelmo.get(str(self.trainidx[q2id]))
else:
q1_elmo = self.develmo.get(str(self.devidx[q1id]))
q2_elmo = self.develmo.get(str(self.devidx[q2id]))
q1_w2v = features.encode(q1, self.word2vec)
q1_elmo_bottom = [
np.concatenate([q1_w2v[i], q1_elmo[0][i]])
for i in range(len(q1_w2v))
]
q1_elmo_middle = [
np.concatenate([q1_w2v[i], q1_elmo[1][i]])
for i in range(len(q1_w2v))
]
q1_elmo_top = [
np.concatenate([q1_w2v[i], q1_elmo[2][i]])
for i in range(len(q1_w2v))
]
q2_w2v = features.encode(q2, self.word2vec)
q2_elmo_bottom = [
np.concatenate([q2_w2v[i], q2_elmo[0][i]])
for i in range(len(q2_w2v))
]
q2_elmo_middle = [
np.concatenate([q2_w2v[i], q2_elmo[1][i]])
for i in range(len(q2_w2v))
]
q2_elmo_top = [
np.concatenate([q2_w2v[i], q2_elmo[2][i]])
for i in range(len(q2_w2v))
]
# X.append(self.simbow.score(q1, q1_w2v, q2, q2_w2v))
X.append(self.simbow.score(q1, q1_elmo_bottom, q2, q2_elmo_bottom))
X.append(self.simbow.score(q1, q1_elmo_middle, q2, q2_elmo_middle))
X.append(self.simbow.score(q1, q1_elmo_top, q2, q2_elmo_top))
return X
def train(self):
logging.info('Training svm.', extra=d)
treekernel = features.TreeKernel(alpha=0,
decay=1,
ignore_leaves=True,
smoothed=False)
self.bm25_model, self.avg_idf, self.bm25_qid_index = features.init_bm25(
traindata=self.trainset, devdata=self.devset, testdata=[])
if not os.path.exists(FEATURE_PATH):
X, y = [], []
for i, query_question in enumerate(self.traindata):
percentage = round(float(i + 1) / len(self.traindata), 2)
print('Preparing traindata: ',
percentage,
i + 1,
sep='\t',
end='\r')
q1id = query_question['q1_id']
q2id = query_question['q2_id']
q1, q2 = query_question['q1'], query_question['q2']
# x = self.get_features(q1id, q1, q2id, q2)
x = []
# x = self.__transform__(q1, q2)
#
# # elmo and word2vec embeddings
q1_elmo = self.trainelmo.get(str(self.trainidx[q1id]))
q1_w2v = features.encode(q1, self.word2vec)
q1_emb = [
np.concatenate([q1_w2v[i], q1_elmo[i]])
for i in range(len(q1_w2v))
]
q2_elmo = self.trainelmo.get(str(self.trainidx[q2id]))
q2_w2v = features.encode(q2, self.word2vec)
q2_emb = [
np.concatenate([q2_w2v[i], q2_elmo[i]])
for i in range(len(q2_w2v))
]
# # translation
# lmprob, trmprob, trlmprob, proctime = self.translation.score_embeddings(q1, q1_emb, q2, q2_emb)
# x.append(trlmprob)
#
# # bm25
# bm25_score = self.bm25_model.get_score(q1, self.bm25_qid_index[q2id], self.avg_idf)
# x.append(bm25_score)
#
# # cosine
# q1_lemma = query_question['q1_lemmas']
# q1_pos = query_question['q1_pos']
# q2_lemma = query_question['q2_lemmas']
# q2_pos = query_question['q2_pos']
# for n in range(1,5):
# try:
# x.append(features.cosine(' '.join(q1), ' '.join(q2), n=n))
# except:
# x.append(0.0)
# try:
# x.append(features.cosine(' '.join(q1_lemma), ' '.join(q2_lemma), n=n))
# except:
# x.append(0.0)
# try:
# x.append(features.cosine(' '.join(q1_pos), ' '.join(q2_pos), n=n))
# except:
# x.append(0.0)
#
# # tree kernels
# q1_token2lemma = dict(zip(query_question['q1_full'], query_question['q1_lemmas']))
# q2_token2lemma = dict(zip(query_question['q2_full'], query_question['q2_lemmas']))
# q1_tree, q2_tree = utils.parse_tree(query_question['q1_tree'], q1_token2lemma), utils.parse_tree(query_question['q2_tree'], q2_token2lemma)
# q1_tree, q2_tree = treekernel.similar_terminals(q1_tree, q2_tree)
# x.append(treekernel(q1_tree, q2_tree))
#
# # frobenius norm
# x.append(features.frobenius_norm(q1_emb, q2_emb))
#
# # softcosine
simbow = self.simbow.score(q1, q1_emb, q2, q2_emb)
x.append(simbow)
for comment in query_question['comments']:
q3id = comment['id']
q3 = comment['tokens']
simbow_q1q3, simbow_q2q3 = 0, 0
if len(q3) > 0:
# x.extend(self.get_features(q1id, q1, q3id, q3))
q3_elmo = self.trainelmo.get(str(self.trainidx[q3id]))
q3_w2v = features.encode(q3, self.word2vec)
q3_emb = [
np.concatenate([q3_w2v[i], q3_elmo[i]])
for i in range(len(q3_w2v))
]
simbow_q1q3 = self.simbow.score(q1, q1_emb, q3, q3_emb)
# simbow_q2q3 = self.simbow.score(q2, q2_emb, q3, q3_emb)
# lmprob, trmprob, trlmprob, proctime = self.translation.score_embeddings(q1, q1_emb, q3, q3_emb)
# bm25_score = self.bm25_model.get_score(q1, self.bm25_qid_index[comment['id']], self.avg_idf)
# x.append(trlmprob)
# x.append(bm25_score)
x.append(simbow_q1q3)
# x.append(simbow_q2q3)
X.append(x)
y.append(query_question['label'])
p.dump(list(zip(X, y)), open(FEATURE_PATH, 'wb'))
else:
f = p.load(open(FEATURE_PATH, 'rb'))
X = list(map(lambda x: x[0], f))
y = list(map(lambda x: x[1], f))
# scale features
self.scaler = MinMaxScaler(feature_range=(-1, 1))
self.scaler.fit(X)
X = self.scaler.transform(X)
clf = LassoCV(cv=10)
self.feat_selector = SelectFromModel(clf)
self.feat_selector.fit(X, y)
X = self.feat_selector.transform(X)
self.model = self.train_svm(trainvectors=X,
labels=y,
c='search',
kernel='search',
gamma='search',
degree='search',
jobs=4)
# self.model = self.train_regression(trainvectors=X, labels=y, c='search', penalty='search', tol='search')
logging.info('Finishing to train svm.')
def validate(self):
logging.info('Validating svm.', extra=d)
treekernel = features.TreeKernel(alpha=0,
decay=1,
ignore_leaves=True,
smoothed=False)
ranking = {}
y_real, y_pred = [], []
for i, q1id in enumerate(self.devset):
ranking[q1id] = []
percentage = round(float(i + 1) / len(self.devset), 2)
print('Progress: ', percentage, i + 1, sep='\t', end='\r')
query = self.devset[q1id]
q1 = query['tokens_proc']
# q1_lemma = query['lemmas']
# q1_pos = query['pos']
# q1_token2lemma = dict(zip(query['tokens'], query['lemmas']))
# q1_tree = utils.parse_tree(query['subj_tree'], q1_token2lemma)
q1_elmo = self.develmo.get(str(self.devidx[q1id]))
q1_w2v = features.encode(q1, self.word2vec)
q1_emb = [
np.concatenate([q1_w2v[i], q1_elmo[i]])
for i in range(len(q1_w2v))
]
duplicates = query['duplicates']
for duplicate in duplicates:
rel_question = duplicate['rel_question']
q2id = rel_question['id']
q2 = rel_question['tokens_proc']
# X = self.get_features(q1id, q1, q2id, q2, set='dev')
# X = self.__transform__(q1, q2)
X = []
q2_elmo = self.develmo.get(str(self.devidx[q2id]))
q2_w2v = features.encode(q2, self.word2vec)
q2_emb = [
np.concatenate([q2_w2v[i], q2_elmo[i]])
for i in range(len(q2_w2v))
]
# # translation
# lmprob, trmprob, trlmprob, proctime = self.translation.score_embeddings(q1, q1_emb, q2, q2_emb)
# X.append(trlmprob)
#
# # bm25
# bm25_score = self.bm25_model.get_score(q1, self.bm25_qid_index[q2id], self.avg_idf)
# X.append(bm25_score)
#
# # cosine
# q2_lemma = rel_question['lemmas']
# q2_pos = rel_question['pos']
# for n in range(1,5):
# try:
# X.append(features.cosine(' '.join(q1), ' '.join(q2), n=n))
# except:
# X.append(0.0)
# try:
# X.append(features.cosine(' '.join(q1_lemma), ' '.join(q2_lemma), n=n))
# except:
# X.append(0.0)
# try:
# X.append(features.cosine(' '.join(q1_pos), ' '.join(q2_pos), n=n))
# except:
# X.append(0.0)
#
# # tree kernel
# q2_token2lemma = dict(zip(rel_question['tokens'], rel_question['lemmas']))
# q2_tree = utils.parse_tree(rel_question['subj_tree'], q2_token2lemma)
# q1_tree, q2_tree = treekernel.similar_terminals(q1_tree, q2_tree)
# X.append(treekernel(q1_tree, q2_tree))
#
# # frobenius norm
# X.append(features.frobenius_norm(q1_emb, q2_emb))
# softcosine
simbow = self.simbow.score(q1, q1_emb, q2, q2_emb)
X.append(simbow)
for comment in duplicate['rel_comments']:
q3id = comment['id']
q3 = comment['tokens_proc']
simbow_q1q3, simbow_q2q3 = 0, 0
if len(q3) > 0:
# X.extend(self.get_features(q1id, q1, q3id, q3, set='dev'))
q3_elmo = self.develmo.get(
str(self.devidx[comment['id']]))
q3_w2v = features.encode(q3, self.word2vec)
q3_emb = [
np.concatenate([q3_w2v[i], q3_elmo[i]])
for i in range(len(q3_w2v))
]
simbow_q1q3 = self.simbow.score(q1, q1_emb, q3, q3_emb)
# simbow_q2q3 = self.simbow.score(q2, q2_emb, q3, q3_emb)
# bm25_score = self.bm25_model.get_score(q1, self.bm25_qid_index[comment['id']], self.avg_idf)
# lmprob, trmprob, trlmprob, proctime = self.translation.score_embeddings(q1, q1_emb, q3, q3_emb)
# X.append(trlmprob)
# X.append(bm25_score)
X.append(simbow_q1q3)
# X.append(simbow_q2q3)
# scale
X = self.scaler.transform([X])
# feature selection
X = self.feat_selector.transform(X)
score = self.model.decision_function(X)[0]
pred_label = self.model.predict(X)[0]
y_pred.append(pred_label)
real_label = 0
if rel_question['relevance'] != 'Irrelevant':
real_label = 1
y_real.append(real_label)
ranking[q1id].append((real_label, score, q2id))
with open('data/ranking.txt', 'w') as f:
for q1id in ranking:
for row in ranking[q1id]:
label = 'false'
if row[0] == 1:
label = 'true'
f.write('\t'.join([
str(q1id),
str(row[2]),
str(0),
str(row[1]), label, '\n'
]))
logging.info('Finishing to validate svm.', extra=d)
return ranking, y_real, y_pred
step=.1,
cv=5,
scoring='roc_auc')
for i in range(0, len(skpipes)):
skpipes[i].append(('rfe_rf' + str(i), cv_rfc))
if fs_type == 2:
#Wrapper Select via model
clf = RandomForestClassifier(n_estimators=200,
max_depth=None,
min_samples_split=3,
criterion='entropy',
random_state=None)
sel = SelectFromModel(
clf, prefit=False, threshold='mean', max_features=None
) #to select only based on max_features, set to integer value and set threshold=-np.inf
for i in range(0, len(skpipes)):
skpipes[i].append(('wrapper_rf' + str(i), sel))
if fs_type == 3: ######Only work if the Target is binned###########
#Univariate Feature Selection - Chi-squared
#will throw error if any negative values in features, so turn off feature normalization, or switch to mutual_info_classif
print('Univariate Feature Selection - Chi2: ')
sel = SelectKBest(chi2, k=k_cnt)
for i in range(0, len(skpipes)):
skpipes[i].append(('ufs' + str(i), sel))
# %%
def train(self):
logging.info('Training svm.', extra=d)
treekernel = features.TreeKernel(alpha=0,
decay=1,
ignore_leaves=True,
smoothed=False)
self.bm25_model, self.avg_idf, self.bm25_qid_index = features.init_bm25(
traindata=self.trainset, devdata=self.devset, testdata=[])
if not os.path.exists(FEATURE_PATH):
X, y = [], []
for i, query_question in enumerate(self.traindata):
percentage = round(float(i + 1) / len(self.traindata), 2)
print('Preparing traindata: ',
percentage,
i + 1,
sep='\t',
end='\r')
q1id = query_question['q1_id']
q2id = query_question['q2_id']
q1, q2 = query_question['q1'], query_question['q2']
# x = self.get_features(q1id, q1, q2id, q2)
x = []
# x = self.__transform__(q1, q2)
#
# # elmo and word2vec embeddings
q1_elmo = self.trainelmo.get(str(self.trainidx[q1id]))
q1_w2v = features.encode(q1, self.word2vec)
q1_emb = [
np.concatenate([q1_w2v[i], q1_elmo[i]])
for i in range(len(q1_w2v))
]
q2_elmo = self.trainelmo.get(str(self.trainidx[q2id]))
q2_w2v = features.encode(q2, self.word2vec)
q2_emb = [
np.concatenate([q2_w2v[i], q2_elmo[i]])
for i in range(len(q2_w2v))
]
# # translation
# lmprob, trmprob, trlmprob, proctime = self.translation.score_embeddings(q1, q1_emb, q2, q2_emb)
# x.append(trlmprob)
#
# # bm25
# bm25_score = self.bm25_model.get_score(q1, self.bm25_qid_index[q2id], self.avg_idf)
# x.append(bm25_score)
#
# # cosine
# q1_lemma = query_question['q1_lemmas']
# q1_pos = query_question['q1_pos']
# q2_lemma = query_question['q2_lemmas']
# q2_pos = query_question['q2_pos']
# for n in range(1,5):
# try:
# x.append(features.cosine(' '.join(q1), ' '.join(q2), n=n))
# except:
# x.append(0.0)
# try:
# x.append(features.cosine(' '.join(q1_lemma), ' '.join(q2_lemma), n=n))
# except:
# x.append(0.0)
# try:
# x.append(features.cosine(' '.join(q1_pos), ' '.join(q2_pos), n=n))
# except:
# x.append(0.0)
#
# # tree kernels
# q1_token2lemma = dict(zip(query_question['q1_full'], query_question['q1_lemmas']))
# q2_token2lemma = dict(zip(query_question['q2_full'], query_question['q2_lemmas']))
# q1_tree, q2_tree = utils.parse_tree(query_question['q1_tree'], q1_token2lemma), utils.parse_tree(query_question['q2_tree'], q2_token2lemma)
# q1_tree, q2_tree = treekernel.similar_terminals(q1_tree, q2_tree)
# x.append(treekernel(q1_tree, q2_tree))
#
# # frobenius norm
# x.append(features.frobenius_norm(q1_emb, q2_emb))
#
# # softcosine
simbow = self.simbow.score(q1, q1_emb, q2, q2_emb)
x.append(simbow)
for comment in query_question['comments']:
q3id = comment['id']
q3 = comment['tokens']
simbow_q1q3, simbow_q2q3 = 0, 0
if len(q3) > 0:
# x.extend(self.get_features(q1id, q1, q3id, q3))
q3_elmo = self.trainelmo.get(str(self.trainidx[q3id]))
q3_w2v = features.encode(q3, self.word2vec)
q3_emb = [
np.concatenate([q3_w2v[i], q3_elmo[i]])
for i in range(len(q3_w2v))
]
simbow_q1q3 = self.simbow.score(q1, q1_emb, q3, q3_emb)
# simbow_q2q3 = self.simbow.score(q2, q2_emb, q3, q3_emb)
# lmprob, trmprob, trlmprob, proctime = self.translation.score_embeddings(q1, q1_emb, q3, q3_emb)
# bm25_score = self.bm25_model.get_score(q1, self.bm25_qid_index[comment['id']], self.avg_idf)
# x.append(trlmprob)
# x.append(bm25_score)
x.append(simbow_q1q3)
# x.append(simbow_q2q3)
X.append(x)
y.append(query_question['label'])
p.dump(list(zip(X, y)), open(FEATURE_PATH, 'wb'))
else:
f = p.load(open(FEATURE_PATH, 'rb'))
X = list(map(lambda x: x[0], f))
y = list(map(lambda x: x[1], f))
# scale features
self.scaler = MinMaxScaler(feature_range=(-1, 1))
self.scaler.fit(X)
X = self.scaler.transform(X)
clf = LassoCV(cv=10)
self.feat_selector = SelectFromModel(clf)
self.feat_selector.fit(X, y)
X = self.feat_selector.transform(X)
self.model = self.train_svm(trainvectors=X,
labels=y,
c='search',
kernel='search',
gamma='search',
degree='search',
jobs=4)
# self.model = self.train_regression(trainvectors=X, labels=y, c='search', penalty='search', tol='search')
logging.info('Finishing to train svm.')
Y = np.concatenate((Positive_y, Negitive_y))
X_train, X_test, y_train, y_test = train_test_split(X,
Y,
test_size=0.33,
random_state=7)
model = XGBClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
thresholds = sort(model.feature_importances_)
for thresh in thresholds:
selection = SelectFromModel(model, threshold=thresh, prefit=True)
select_X_train = selection.transform(X_train)
selection_model = XGBClassifier()
selection_model.fit(select_X_train, y_train)
select_X_test = selection.transform(X_test)
y_pred = selection_model.predict(select_X_test)
predictions = [round(value) for value in y_pred]
accuracy = accuracy_score(y_test, predictions)
print("Thresh=%.3f, n=%d, Accuracy: %.2f%%" %
(thresh, select_X_train.shape[1], accuracy * 100.0))
b = sorted(enumerate(model.feature_importances_),
key=lambda x: x[1],
reverse=True)
a = np.array(b)[:, 0][0:MAX_LEN].astype(np.uint8)
[0.82122905 0.83240223 0.81460674 0.8258427 0.85875706] 0.8305675570530682 ''' bagging_clf = BaggingRegressor(lr, n_estimators=10, max_samples=0.8, max_features=1.0, n_jobs=-1) # here we can even set bootstrap=false to get duplicate samples evaluate_model(bagging_clf) from sklearn.feature_selection import SelectFromModel lr = LogisticRegression(C=20, penalty='l2', tol=1e-8) selector = SelectFromModel(lr, threshold='1.25*median') selector.fit(train_x, train_y) train_x2 = selector.transform(train_x) print(train_x.columns[selector.get_support()]) lr.fit(train_x2, train_y) print(lr.score(train_x2, train_y)) print(lr.score(selector.transform(test_x),test_y)) cvs = cross_val_score(lr, selector.transform(train_X), train_Y, cv=5) print(cvs) print(np.mean(cvs), np.std(cvs)) ''' 0.8475120385232745
scaler = MinMaxScaler()
data1 = scaler.fit_transform(data[numeric_feature])
#calibrate the categorical features,
encoder = OneHotEncoder(categories = 'auto', sparse = False)
data2 = encoder.fit_transform(data[categorical_feature])
#merge preprocessed features
x = np.append(data1, data2, axis = 1)
print('number of features after preprocessing: %d' % len(x[0]))
print("")
#use extra trees for feature selection
clf = ExtraTreesClassifier(n_jobs=-1, random_state=0)
clf = clf.fit(x,y)
model = SelectFromModel(clf, prefit=True)
x = model.transform(x)
print('number of features after feature selection: %d' % len(x[0]))
print("")
#calculuate feature importance and output top 30 features with high importance
categorical_name = encoder.get_feature_names(categorical_feature)
feature_name = np.append(numeric_feature, categorical_name)
feature_importance = clf.feature_importances_
print('average feature importance: %f' % feature_importance.mean())
print("")
importance = dict(zip(feature_name, feature_importance))
importance_sorted = sorted(importance.items(), key = lambda x: x[1], reverse=True)
print("top 30 features with high importance:")
print(importance_sorted[0:30])
print("")
chi_support = chi_selector.get_support() chi_feature = X.loc[:,chi_support].columns.tolist() print(str(len(chi_feature)), 'selected features') from sklearn.feature_selection import RFE from sklearn.linear_model import LogisticRegression rfe_selector = RFE(estimator=LogisticRegression(max_iter=1000), n_features_to_select=num_feats, step=10, verbose=5) rfe_selector.fit(X_norm, y) rfe_support = rfe_selector.get_support() rfe_feature = X.loc[:,rfe_support].columns.tolist() print(str(len(rfe_feature)), 'selected features') from sklearn.feature_selection import SelectFromModel from sklearn.linear_model import LogisticRegression embeded_lr_selector = SelectFromModel(LogisticRegression(solver='saga', penalty="l1", max_iter=1000), max_features=num_feats) embeded_lr_selector.fit(X_norm, y) embeded_lr_support = embeded_lr_selector.get_support() embeded_lr_feature = X.loc[:,embeded_lr_support].columns.tolist() print(str(len(embeded_lr_feature)), 'selected features') from sklearn.feature_selection import SelectFromModel from sklearn.ensemble import RandomForestClassifier embeded_rf_selector = SelectFromModel(RandomForestClassifier(n_estimators=100), max_features=num_feats) embeded_rf_selector.fit(X, y) embeded_rf_support = embeded_rf_selector.get_support() embeded_rf_feature = X.loc[:,embeded_rf_support].columns.tolist() print(str(len(embeded_rf_feature)), 'selected features')
optimizers = ['rmsprop', 'adam', 'adadelta']
return {
'deep__batch_size': batches,
'deep__epochs': epochs,
'deep__act': activation,
'deep__drop': dropout,
'deep__optimizer': optimizers
}
for i in range(len(multi_XGB.estimators_)):
threshold = np.sort(multi_XGB.estimators_[i].feature_importances_)
for thres in threshold:
selection = SelectFromModel(multi_XGB.estimators_[i],
threshold=thres,
prefit=True)
select_x_train = selection.transform(x_train)
select_x_test = selection.transform(x_test)
select_x_pred = selection.transform(x_pred)
def build_model(drop=0.5, optimizer='adam', act='relu'):
if act == 'leaky':
act = leaky
inputs = Input(shape=(select_x_train.shape[1], ))
x = Dense(51, activation=act)(inputs)
x = Dropout(drop)(x)
x = Dense(150, activation=act)(x)
x = Dropout(drop)(x)
x = Dense(300, activation=act)(x)
@简介：对特征进行嵌入式选择
@author: Jian
"""
import time
import pickle
from sklearn.feature_selection import SelectFromModel
from sklearn.svm import LinearSVC
t_start = time.time()
"""读取特征"""
features_path = './data_tfidf_100000.pkl' #tfidf特征的路径
fp = open(features_path, 'rb')
x_train, y_train, x_test = pickle.load(fp)
fp.close()
"""进行特征选择"""
alo_name = 'lsvc_l2'
lsvc = LinearSVC(penalty='l2', C=1.0, dual=True).fit(x_train, y_train)
slt = SelectFromModel(lsvc, prefit=True)
x_train_s = slt.transform(x_train)
x_test_s = slt.transform(x_test)
"""保存选择后的特征至本地"""
num_features = x_train_s.shape[1]
data_path = './' + features_path.split(
'.')[-2] + '_select_' + alo_name + '_' + str(num_features) + '.pkl'
data_f = open(data_path, 'wb')
pickle.dump((x_train_s, y_train, x_test_s), data_f)
data_f.close()
t_end = time.time()
print("特征选择完成，选择{}个特征，共耗时{}min".format(num_features, (t_end - t_start) / 60))
def run_grid_pipeline(self, features, labels, standardization_colms,
parameters, estimator,
feature_selection_threshold_type):
# Preprocessing for numerical data
numerical_transformer = StandardScaler()
# Preprocessing for categorical data
categorical_transformer = OneHotEncoder(handle_unknown='ignore')
# Bundle preprocessing for numerical and categorical data
preprocessor = ColumnTransformer(
transformers=[
('num', numerical_transformer, standardization_colms),
# ('cat', categorical_transformer, self.onehot_colms)
# ], n_jobs = self.n_jobs)
],
n_jobs=self.n_jobs,
remainder='passthrough')
feature_selection_clf = RandomForestClassifier(
random_state=self.random_state, n_jobs=self.n_jobs)
feature_selection_model = SelectFromModel(
feature_selection_clf, threshold=feature_selection_threshold_type)
grid = GridSearchCV(estimator=estimator,
param_grid=parameters,
cv=5,
scoring='accuracy',
refit=True,
n_jobs=-1)
pipeline = Pipeline(steps=[(
'preprocessor',
preprocessor), ('feature_selection',
feature_selection_model), ('grid_search', grid)])
pipeline.fit(features, labels)
def print_results(results):
print('BEST PARAMS: {}\n'.format(results.best_params_))
means = results.cv_results_['mean_test_score']
stds = results.cv_results_['std_test_score']
for mean, std, params in zip(means, stds,
results.cv_results_['params']):
print('{} (+/-{}) for {}'.format(round(mean, 3),
round(std * 2, 3), params))
print_results(pipeline['grid_search'])
# print(features.columns)
feature_selection_model = pipeline['feature_selection']
selected_features = feature_selection_model.transform(features)
selected_features = pd.DataFrame(
feature_selection_model.inverse_transform(selected_features),
index=features.index,
columns=features.columns)
self.selected_columns = selected_features.columns[
selected_features.var() != 0]
print(
'\nColumns selected for {0} threshold'.format(
feature_selection_threshold_type), self.selected_columns)
# print('\nBest estimator:\n')
# print(pipeline['grid_search'].best_estimator_)
# print(pipeline['grid_search'].best_score_)
# print(pipeline['grid_search'].best_params_)
# print(pipeline['grid_search'].scorer_)
return pipeline
y,
test_size=0.10)
vectorizer = TfidfVectorizer(binary=True, ngram_range=(1, 2))
X_train_rf = vectorizer.fit_transform(X_train)
rf = RandomForestClassifier(n_estimators=400,
verbose=100,
n_jobs=-1,
random_state=0,
max_samples=5000)
rf.fit(X_train_rf, y_train)
feature_selector = SelectFromModel(rf, prefit=True, max_features=100000)
svc_set = pd.concat([X_train, y_train], axis=1)
svc_set = svc_set.sample(100000, random_state=0)
svc_X = svc_set['review']
svc_y = svc_set['label']
svc_X = vectorizer.transform(svc_X)
svc_X = feature_selector.transform(svc_X)
svc = SVC(cache_size=1000, random_state=0)
svc.fit(svc_X, svc_y)
final_pipe = make_pipeline(vectorizer, feature_selector, svc)
def feature_selection(self):
onehot_features = self.original_features
onehot_labels = self.original_labels
onehot_encoder = OneHotEncoder(handle_unknown='error', sparse=False)
onehot_encoder.fit(onehot_features[self.onehot_colms])
onehot_transformed_colms = onehot_encoder.get_feature_names(
self.onehot_colms)
onehot_transformed_features = onehot_encoder.transform(
onehot_features[self.onehot_colms])
onehot_features = onehot_features.join(pd.DataFrame(
onehot_transformed_features,
index=onehot_features.index,
columns=onehot_transformed_colms),
how='inner')
# print(onehot_features.info())
# print(onehot_transformed_colms)
onehot_features = onehot_features.drop(columns=self.onehot_colms)
# print(onehot_features.info())
# print(self.original_features.loc[0:5,'Region'])
# print(onehot_features.loc[0:5, ['Region_1', 'Region_2', 'Region_3', 'Region_4', 'Region_5', 'Region_6', 'Region_7', 'Region_8', 'Region_9'] ] )
sss = StratifiedShuffleSplit(n_splits=1,
train_size=self.train_ratio,
random_state=self.random_state)
for train_indx, test_indx in sss.split(onehot_features, onehot_labels):
# print(len(train_indx)/len(features), len(test_indx)/len(features))
# print('% Survived:', labels[test_indx].mean())
# Using RandomForestClassifier gives non-linear decision boundary
clf = RandomForestClassifier(random_state=self.random_state,
n_jobs=self.n_jobs)
# Using LogisticRegression (default L1) gives linear decision boundary
# clf = LogisticRegression()
clf.fit(onehot_features.iloc[train_indx],
onehot_labels.iloc[train_indx])
# Using mean threshold in SelectFromModel
feature_selection_model = SelectFromModel(clf,
prefit=True,
threshold='mean')
selected_features = feature_selection_model.transform(
onehot_features.iloc[train_indx])
selected_features = pd.DataFrame(
feature_selection_model.inverse_transform(selected_features),
index=onehot_features.iloc[train_indx].index,
columns=onehot_features.iloc[train_indx].columns)
self.selected_columns_mean = selected_features.columns[
selected_features.var() != 0]
print('Mean threshold:', self.selected_columns_mean)
# Using Median threshold for SelectFromModel
feature_selection_model = SelectFromModel(clf,
prefit=True,
threshold='median')
selected_features = feature_selection_model.transform(
onehot_features.iloc[train_indx])
selected_features = pd.DataFrame(
feature_selection_model.inverse_transform(selected_features),
index=onehot_features.iloc[train_indx].index,
columns=onehot_features.iloc[train_indx].columns)
self.selected_columns_median = selected_features.columns[
selected_features.var() != 0]
print('Median threshold', self.selected_columns_median)
def gbdt_feature_selection(fe_name, matrix_x_temp, label_y, th):
# SelectfromModel
clf = GradientBoostingClassifier(n_estimators=200, random_state=100)
clf.fit(matrix_x_temp, label_y)
sfm = SelectFromModel(clf, prefit=True, threshold=th)
matrix_x = sfm.transform(matrix_x_temp)
# 打印出有多少特征重要性非零的特征
feature_score_dict = {}
for fn, s in zip(fe_name, clf.feature_importances_):
feature_score_dict[fn] = s
m = 0
for k in feature_score_dict:
if feature_score_dict[k] == 0.0:
m += 1
print 'number of not-zero features:' + str(len(feature_score_dict) - m)
# 打印出特征重要性
feature_score_dict_sorted = sorted(feature_score_dict.items(),
key=lambda d: d[1],
reverse=True)
print 'feature_importance:'
for ii in range(len(feature_score_dict_sorted)):
print feature_score_dict_sorted[ii][0], feature_score_dict_sorted[ii][
1]
print '\n'
f = open('../eda/A_gbdt_feature_importance.txt', 'w')
f.write(th)
f.write('\nRank\tFeature Name\tFeature Importance\n')
for i in range(len(feature_score_dict_sorted)):
f.write(
str(i) + '\t' + str(feature_score_dict_sorted[i][0]) + '\t' +
str(feature_score_dict_sorted[i][1]) + '\n')
f.close()
# 打印具体使用了哪些字段
how_long = matrix_x.shape[1] # matrix_x 是 特征选择后的 输入矩阵
feature_used_dict_temp = feature_score_dict_sorted[:how_long]
feature_used_name = []
for ii in range(len(feature_used_dict_temp)):
feature_used_name.append(feature_used_dict_temp[ii][0])
print 'feature_chooesed:'
for ii in range(len(feature_used_name)):
print feature_used_name[ii]
print '\n'
f = open('../eda/A_gbdt_feature_chose.txt', 'w')
f.write('Feature Chose Name :\n')
for i in range(len(feature_used_name)):
f.write(str(feature_used_name[i]) + '\n')
f.close()
# 找到未被使用的字段名
feature_not_used_name = []
for i in range(len(fe_name)):
if fe_name[i] not in feature_used_name:
feature_not_used_name.append(fe_name[i])
# 生成一个染色体（诸如01011100这样的）
chromosome_temp = ''
feature_name_ivar = fe_name[:-1]
for ii in range(len(feature_name_ivar)):
if feature_name_ivar[ii] in feature_used_name:
chromosome_temp += '1'
else:
chromosome_temp += '0'
print 'Chromosome:'
print chromosome_temp
joblib.dump(chromosome_temp, '../config/chromosome.pkl')
print '\n'
return matrix_x, feature_not_used_name[:], len(feature_used_name)
""" # #加载库 from sklearn.ensemble import RandomForestClassifier from sklearn import datasets from sklearn.feature_selection import SelectFromModel import numpy as np #加载数据 iris = datasets.load_iris() features = iris.data target = iris.target #创建随机森林分类器对象 randomforest = RandomForestClassifier(random_state=0, n_jobs=-1) #创建对象，选择重要性大于或等于阈值的特征 selector = SelectFromModel(randomforest, threshold=0.3) #使用选择器创建新的特征矩阵 features_important = selector.fit_transform(features, target) #使用重要特征训练随机森林模型 model = randomforest.fit(features_important, target) #计算特征的重要性 importances = model.feature_importances_ #查看模型中每个特征的重要程度 print(importances) #将特征的重要性按降序排列 indices = np.argsort(importances)[::-1] #按照特征的重要性对特征名称重新排序 names = [iris.feature_names[i] for i in indices]
fit_mod = sel.fit(data_np, target_np)
print(sel.ranking_)
sel_idx = fit_mod.get_support()
if fs_type == 2:
#Wrapper Select via model
if binning == 0:
clf = DecisionTreeClassifier(criterion='gini',
splitter='best',
max_depth=None,
min_samples_split=3,
min_samples_leaf=1,
max_features=None,
random_state=rand_st)
sel = SelectFromModel(
clf, prefit=False, threshold='mean', max_features=None
) #to select only based on max_features, set to integer value and set threshold=-np.inf
print('Wrapper Select: ')
if binning == 1:
rgr = '''Unused in this homework'''
sel = SelectFromModel(rgr,
prefit=False,
threshold='mean',
max_features=None)
print('Wrapper Select: ')
fit_mod = sel.fit(data_np, target_np)
sel_idx = fit_mod.get_support()
if fs_type == 3:
if binning == 1: ######Only work if the Target is binned###########
df = df.set_index('trace:id')
df_join = df2['status']
df_all = df.join(df_join, how='inner')
df_all = df_all.dropna()
y = df_all.pop('status')
df_all = df_all.drop('qr', axis=1)
print(y)
print(len(df_all))
# plot high dim data
plot_data(df_all, y, 'TSNE', 2)
#print(df_all.columns)
pipe = make_pipeline(
SelectFromModel(estimator=RandomForestClassifier(
n_estimators=100, max_depth=2, random_state=0)),
LogisticRegression(solver='lbfgs', multi_class='auto', max_iter=5000))
lr = LogisticRegression(solver='lbfgs',
multi_class='auto',
max_iter=5000,
class_weight='balanced')
#rf = RandomForestClassifier(n_estimators=100, max_depth=2, random_state=0)
rf = RandomForestClassifier(n_estimators=500,
max_depth=5,
random_state=0,
class_weight={
'nok': 4,
'ok': 1
})
print(cross_val_score(lr, df_all, y, scoring='accuracy', cv=5).mean())
print(cross_val_score(rf, df_all, y, scoring='accuracy', cv=5).mean())
error_train = mean_squared_error(y_tr, y_tr_pred)
error_test = mean_squared_error(y_ts, y_ts_pred)
error_std_train = mean_squared_error(y_std_tr, y_std_tr_pred)
error_std_test = mean_squared_error(y_std_ts, y_std_ts_pred)
print("---------------------------------------")
print("# Mean Squared Error:")
print(regressor_name + " MSE train: %.3f, test: %.3f" % (error_train, error_test))
print(regressor_name + " STD MSE train: %.3f, test: %.3f" % (error_std_train, error_std_test))
# Performance improvement
print("\n\n\n======================")
print("PERFORMANCE IMPROVEMENT")
clf = LassoCV(cv=5)
sfm = SelectFromModel(clf, threshold=0.25)
sfm.fit(x, y)
n_features = sfm.transform(x).shape[1]
while n_features > 4:
sfm.threshold += 0.1
x_new = sfm.transform(x)
n_features = x_new.shape[1]
# Standardizing
sc_x = StandardScaler()
x_std_new = sc_x.fit_transform(x_new)
sc_y = StandardScaler()
y_std = sc_y.fit_transform(y[:, np.newaxis]).flatten()
# Splitting train and test data
drop=False, nan=False),
'clipper': OutliersClipper(columns=['LotFrontage', 'LotArea', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal']),
'combinations': FeatureProduct(columns=['LotFrontage', 'BsmtFinSF1', 'MasVnrArea', '1stFlrSF', 'GarageArea', 'TotalBsmtSF', 'GrLivArea']),
'dropper__drop': ['LotFrontage_nan', 'MasVnrArea_nan', 'GarageYrBlt_nan'],
'main_imputer': HotDeckFullImputer(col_k_pairs=[('LotFrontage', None), ('MasVnrArea', None), ('GarageYrBlt', None)],
default_k=5),
'poly': PolynomialsAdder(powers_per_column={'LotFrontage': [2], 'LotArea': [2], 'MasVnrArea': [2], 'BsmtFinSF1': [2], 'BsmtFinSF2': [2], 'BsmtUnfSF': [2], 'TotalBsmtSF': [2], '1stFlrSF': [2], '2ndFlrSF': [2], 'LowQualFinSF': [2], 'GrLivArea': [2], 'GarageArea': [2], 'WoodDeckSF': [2], 'OpenPorchSF': [2], 'EnclosedPorch': [2], '3SsnPorch': [2], 'ScreenPorch': [2], 'PoolArea': [2], 'MiscVal': [2]}),
'predictor': DecisionTreeRegressor(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'),
'reduce_dim': SelectFromModel(estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=8,
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),
norm_order=1, prefit=False, threshold=None),
'simple_imputer': FillNaTransformer(from_dict={},
mean=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], median=[],
nan_flag=['LotFrontage', 'MasVnrArea', 'GarageYrBlt'], zero=[])},
'score': 37866.96889728187,
'std': 5359.25597193946},
'Lasso': {'params': {'binner': None,
'binner2': CustomBinaryBinner(configuration={'LotFrontage': {'values': [182.0]}, 'LotArea': {'values': [215245]}, 'MasVnrArea': {'values': [1378.0]}, 'BsmtFinSF1': {'values': [2188]}, 'BsmtFinSF2': {'values': [1120]}, 'BsmtUnfSF': {'values': [2336]}, 'TotalBsmtSF': {'values': [3206]}, '1stFlrSF': {'values': [3228]}, '2ndFlrSF': ... [2010.0]}, 'GarageCars': {'values': [4]}, 'MoSold': {'values': [12]}, 'YrSold': {'values': [2010]}},
drop=False, nan=False),
'clipper': None,
'combinations': FeatureProduct(columns=['LotFrontage', 'BsmtFinSF1', 'MasVnrArea', '1stFlrSF', 'GarageArea', 'TotalBsmtSF', 'GrLivArea']),
'dropper__drop': ['LotFrontage_nan', 'MasVnrArea_nan', 'GarageYrBlt_nan'],
'main_imputer': HotDeckFullImputer(col_k_pairs=[('LotFrontage', None), ('MasVnrArea', None), ('GarageYrBlt', None)],
train['Trap'] = lbl.transform(train['Trap'].values)
lbl.fit(list(train['CodeSum_x'].values)) # + list(test['CodeSum_x'].values))
train['CodeSum_x'] = lbl.transform(train['CodeSum_x'].values)
lbl.fit(list(train['CodeSum_y'].values)) # + list(test['CodeSum_y'].values))
train['CodeSum_y'] = lbl.transform(train['CodeSum_y'].values)
########################################################################################################################
train = train.astype(float)
#train = train.loc[:,(train != -1).any(axis=0)]
label = train.WnvPresent
train = train.drop('WnvPresent', axis=1)
sfm = SelectFromModel(LinearSVC(penalty='l1', loss='squared_hinge',
dual=False))
data = sfm.fit_transform(train, label)
data = preprocessing.scale(data)
#data = preprocessing.scale(train)
transformer = FunctionTransformer(np.log1p, validate=True)
transformer.transform(data)
data = preprocessing.normalize(data, norm='l2')
feature_cols = train.columns
databackup = data
data = pd.DataFrame(sfm.inverse_transform(data),
index=train.index,
columns=feature_cols)
selCols = data.columns[data.var() != 0]
data = data[selCols]
def main():
# Input datasets as pandas dataframes
df_original = pd.read_csv('Predictive Modelling Train.txt',
decimal=",",
sep="|")
df_original_predict = pd.read_csv('Predictive Modelling Test.txt',
decimal=",",
sep="|")
#---------------------------------------------------------------------------#
# 3- DATA MANIPULATION #
#---------------------------------------------------------------------------#
# Train - Drop ID and remove duplicates in train set
df_original = df_original.drop(['ID'], 1)
df_original = df_original.drop_duplicates()
# Predict - Save ID of dataframe to predict
test_id = df_original_predict.ID
# Predict - Drop ID and convert to numpy array
test_preprocessed = df_original_predict.drop(["ID"], 1)
X_predict = np.array(test_preprocessed)
# Train - Separate class and features
X = np.array(df_original.drop(['TARGET'], axis=1))
y = np.array(df_original.TARGET.values)
# Split train dataset
X_to_balance, X_real_test, y_to_balance, y_real_test = train_test_split(
X, y, test_size=test_size_value, random_state=random_state_value)
# Oversample data (TARGET=1) to balance
sm = SMOTE(kind='regular')
X_balanced, y_balanced = sm.fit_sample(X_to_balance, y_to_balance)
# Create new features
df_real = feature_engineering_df(X_real_test, df_original)
df_balanced = feature_engineering_df(X_balanced, df_original)
df_test_processed = feature_engineering_df(X_predict, df_original)
# Convert pandas dataframes to numpy arrays
X_balanced = np.array(df_balanced)
X_real_test = np.array(df_real)
X_predict = np.array(df_test_processed)
#---------------------------------------------------------------------------#
# 4- FEATURE SELECTION #
#---------------------------------------------------------------------------#
# Define classifier for feature importance and selection
clf1 = ExtraTreesClassifier(n_jobs=-1, random_state=random_state_value)
selector = clf1.fit(X_balanced, y_balanced)
# Choose best features
fs = SelectFromModel(selector, prefit=True)
# Discard non selected features
X_real_test = fs.transform(X_real_test)
X_balanced = fs.transform(X_balanced)
X_predict_final = fs.transform(X_predict)
#---------------------------------------------------------------------------#
# 5- MODEL TRAIN + FIT #
#---------------------------------------------------------------------------#
# Define prediction classifier and fit
clf2 = KNeighborsClassifier(n_jobs=-1, n_neighbors=9)
clf2.fit(X_balanced, y_balanced)
#---------------------------------------------------------------------------#
# 6- MODEL EVALUATION #
#---------------------------------------------------------------------------#
# !!! IMPORTANT: COMMENT WHOLE BLOCK WHEN DOING REAL TRAINING AND PREDCTION (test_size_value = 0)
# Print used classifiers and their parameters
print("Feature selection classifier: ", clf1, "\n")
print("Model classifier: ", clf2, "\n")
# Calculate predictions
y_pred = clf2.predict_proba(X_real_test)[:, 1]
y_pred_int = clf2.predict(X_real_test)
# Evaluate model
print("Roc AUC: ", roc_auc_score(y_real_test, y_pred, average='macro'))
accuracy = clf2.score(X_real_test, y_real_test)
print("Accuracy: ", accuracy)
print("f1 Score: ", f1_score(y_real_test, y_pred_int, average='macro'))
# Hardcoded benchmark of filling prediction with most common class (0)
zeros_benchmark = 1 - 7477 / 459992
print("Filling with 0's benchmark: ", zeros_benchmark)
# Fixed accuracy with zeros_benchmark
print("Fixed accuracy with benchmark: ",
(accuracy - zeros_benchmark) / (1 - zeros_benchmark))
# Confusion matrix
conf_matrix = confusion_matrix(y_real_test, y_pred_int)
print("\n[Confusion Matrix]: \n", conf_matrix)
print(
"\n------------------------------------------------------------------\n\n"
)
#---------------------------------------------------------------------------#
# 7- PREDICTION AND SUBMISSION #
#---------------------------------------------------------------------------#
# Make prediction
predict_submission = clf2.predict(X_predict_final)
# Save in csv
submission = pd.DataFrame({"ID": test_id, "TARGET": predict_submission})
submission.to_csv("submission.csv", index=False, sep="|")
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
clf = RandomForestClassifier(n_estimators=50, max_features='sqrt')
clf = clf.fit(data[:train_objs_num], y)
features = pd.DataFrame()
features['feature'] = data.columns
features['importance'] = clf.feature_importances_
features.sort_values(by=['importance'], ascending=True, inplace=True)
features.set_index('feature', inplace=True)
features.plot(kind='barh', figsize=(25, 25))
from sklearn.feature_selection import SelectFromModel
model = SelectFromModel(clf, prefit=True)
train_reduced = model.transform(data[:train_objs_num])
test_reduced = model.transform(data[train_objs_num:])
print(train_reduced.shape,test_reduced.shape)
from sklearn.ensemble.gradient_boosting import GradientBoostingClassifier
from sklearn.feature_selection import SelectKBest
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
logreg = LogisticRegression()
idx.append(k)
# 计算这一类特征的权值系数均值
mean = coef / len(idx)
self.coef_[i][idx] = mean
return self
import scipy.io as sio
image_path = r'./mat/simple.mat'
image_D = sio.loadmat(image_path)
X = image_D['dataset']
y = image_D['label2']
'''
iris = load_iris()
X, y = iris.data, iris.target
y.resize((150,1))
y = np.hstack((y,np.zeros((150,1))))
'''
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
X2 = np.delete(X,range(16,32,1),1)
model = ExtraTreesClassifier()
model.fit(X2, y)
print(model.feature_importances_)
#带L1和L2惩罚项的逻辑回归作为基模型的特征选择
#参数threshold为权值系数之差的阈值
a = SelectFromModel(LR(threshold=0.5, C=0.1)).fit_transform(X, y)
lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(X, y)
a = SelectFromModel(lsvc,prefit=True)
a = a.transform(X)
print("X_new 共有 %s 个特征"%a.shape[1])
madelonY = madelon['Class'].copy().values
adult_trgX, adult_tstX, adult_trgY, adult_tstY = ms.train_test_split(
adultX, adultY, test_size=0.3, random_state=0, stratify=adultY)
madelon_trgX, madelon_tstX, madelon_trgY, madelon_tstY = ms.train_test_split(
madelonX, madelonY, test_size=0.3, random_state=0, stratify=madelonY)
pipeA = Pipeline([('Scale', StandardScaler()),
('MLP',
MLPClassifier(max_iter=2000,
early_stopping=True,
random_state=55))])
pipeM = Pipeline([('Scale', StandardScaler()),
('Cull1',
SelectFromModel(RandomForestClassifier(random_state=1),
threshold='median')),
('Cull2',
SelectFromModel(RandomForestClassifier(random_state=2),
threshold='median')),
('Cull3',
SelectFromModel(RandomForestClassifier(random_state=3),
threshold='median')),
('Cull4',
SelectFromModel(RandomForestClassifier(random_state=4),
threshold='median')),
('MLP',
MLPClassifier(max_iter=2000,
early_stopping=True,
random_state=55))])
d = adultX.shape[1]
