class QuantileTransformerImpl():
def __init__(self,
n_quantiles=1000,
output_distribution='uniform',
ignore_implicit_zeros=False,
subsample=100000,
random_state=None,
copy=True):
self._hyperparams = {
'n_quantiles': n_quantiles,
'output_distribution': output_distribution,
'ignore_implicit_zeros': ignore_implicit_zeros,
'subsample': subsample,
'random_state': random_state,
'copy': copy
}
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def transform(self, X):
return self._sklearn_model.transform(X)
def fit_model(model, scaler='quantileNormal'):
#Usar todos os dados para garantir a representacao de todos os genres
X, y, genre_mapping = loadFeatures_NoSplit()
if scaler=='standard':
norm = StandardScaler()
elif scaler=='minMax':
norm = MinMaxScaler()
elif scaler=='maxAbs':
norm = MaxAbsScaler()
elif scaler=='robust':
norm = RobustScaler(quantile_range=(25, 75))
elif scaler=='quantileUniform':
norm = QuantileTransformer(output_distribution='uniform')
elif scaler=='quantileNormal':
norm = QuantileTransformer(output_distribution='normal')
elif scaler=='L2Norm':
norm = Normalizer()
if model == 'RF':
classifier = RandomForestClassifier(n_estimators=100, criterion='entropy', max_features=1, min_samples_leaf=6, min_samples_split=5)
elif model == 'SVM':
classifier = SVC(kernel='linear', gamma=0.15043359874882642, C=37.04299991666622)
elif model == 'LR':
classifier = LogisticRegression(solver='lbfgs', penalty='l2', C=12.860378815138466, multi_class='multinomial', warm_start=True)
elif model == 'kNN':
classifier = KNeighborsClassifier(algorithm='ball_tree', leaf_size=31, n_neighbors=29)
elif model == 'MLP':
classifier = MLPClassifier(activation='relu', alpha=0.05440165708835292, batch_size=358, solver='adam')
elif model == 'SGD':
classifier = SGDClassifier(penalty='elasticnet', loss='log', alpha=0.02011798244191186, max_iter=50)
elif model == 'extraTrees':
classifier = ExtraTreesClassifier(n_estimators=50, criterion='gini', max_features=3, min_samples_leaf=5, min_samples_split=8)
else:
raise ValueError('Classifier not recognized {}.'.format(model))
#Usar e guardar o pipeline para aplicar os mesmos passos aos novos dados de teste
pipe = Pipeline([('normalize', norm), ('reduce_dim', LDA()),('classify', classifier)])
pipe.fit(X, y)
#Previsoes com dados novos
# score = pipe.score(X,y)
# features = pd.read_csv('sampleFeatures.csv', index_col=0, header=[0, 1, 2])
# X = features.iloc[:, 0:518].values
# predict = pipe.predict(X)
# print("Score is %s and Prediction is %s with model %s" % (score, predict, model))
joblib.dump(pipe, 'final_model.pkl')
result = "%s Model using %s scaler" % (model, scaler)
return result
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def __init__(self, n_quantiles=1000, output_distribution='uniform', ignore_implicit_zeros=False, subsample=100000, random_state=None, copy=True):
self._hyperparams = {
'n_quantiles': n_quantiles,
'output_distribution': output_distribution,
'ignore_implicit_zeros': ignore_implicit_zeros,
'subsample': subsample,
'random_state': random_state,
'copy': copy}
self._wrapped_model = Op(**self._hyperparams)
def normalize(X, scaler='quantileNormal'):
if scaler=='standard':
X = StandardScaler().fit_transform(X)
elif scaler=='minMax':
X = MinMaxScaler().fit_transform(X)
elif scaler=='maxAbs':
X = MaxAbsScaler().fit_transform(X)
elif scaler=='robust':
X = RobustScaler(quantile_range=(25, 75)).fit_transform(X)
elif scaler=='quantileUniform':
X = QuantileTransformer(output_distribution='uniform').fit_transform(X)
elif scaler=='quantileNormal':
X = QuantileTransformer(output_distribution='normal').fit_transform(X)
elif scaler=='L2Norm':
X = Normalizer().fit_transform(X)
else:
raise ValueError('Scaler is not defined: %s' % scaler)
return X
def quantile_scale_map(self,
col_need,
distribution='uniform',
drop_origin_col=False):
"""
:param col_need:
:param distribution: 'uniform' (default) or 'normal'
:param drop_origin_col:
:return:
"""
self.quantile_transform = QuantileTransformer(
output_distribution=distribution)
array_quantile = self.quantile_transform.fit_transform(
self.df.loc[:, col_need])
self._scale_map(array=array_quantile,
column_name=col_need,
suffix="_q{}Map".format(distribution.capitalize()),
drop_origin_columns=drop_origin_col)
def main():
X, y = load_wine()
distributions = [
#('Unscaled data', X),
#('Data after standard scaling', StandardScaler().fit_transform(X)),
#('Data after min-max scaling', MinMaxScaler().fit_transform(X)),
#('Data after max-abs scaling', MaxAbsScaler().fit_transform(X)),
#('Data after robust scaling', RobustScaler(quantile_range=(25, 75)).fit_transform(X)),
('Data after quantile transformation (uniform pdf)', QuantileTransformer(output_distribution='uniform').fit_transform(X)),
#('Data after quantile transformation (gaussian pdf)', QuantileTransformer(output_distribution='normal').fit_transform(X)),
#('Data after sample-wise L2 normalizing', Normalizer().fit_transform(X)) ## this one sucked
]
for label, scaled_X in distributions:
print '========================'
print label
print '========================'
scaled_X_df = pd.DataFrame(scaled_X, index=X.index, columns=X.columns)
machine_learn(scaled_X_df, y)
print '========================'
print ''
make_plots(scores)
def getClassWeights(self, weightType, dataSet=None):
if not weightType in [
"Freq", "MedianFreq", "1x", "2x", "division", "relativeToMin",
"quantile"
]:
raise ValueError(
"Wrong weights calc type given! Valid arguments are [Freq, MedianFreq, 1x, 2x, division, relativeToMin, quantile]"
)
# get class weights because of imbalanced dataset (e.g. a lot of road and buildings)
print("Calculate class ", weightType, " weights...")
# only calculate the weights from a specific split of the dataset. For performance reasons
# PART = 1 would be the total dataset
PART = 10
classCount = np.zeros(self.config["classes"])
# count all the classes in every given mask image
for i in range(int(self.config["trainSize"] / PART)):
labelImg = self.getImage(i, "trainLabel").flatten()
labelClassCount = np.bincount(labelImg,
minlength=self.config["classes"])
classCount += labelClassCount
if i % int(1000 / PART) == 0:
print("Label image ", i, "/", self.config["trainSize"] / PART)
print("Class count: ", classCount.shape, classCount)
#choose class weights type
#Frequency
if weightType == "Freq":
classWeights = np.median(classCount) / classCount
#Median Frequency
elif weightType == "MedianFreq":
classWeights = np.median(np.median(classCount) / classCount) / (
np.median(classCount) / classCount)
# Simple Total/ClassCount
elif weightType == "1x":
classWeights = 1 - (classCount / classCount.sum() * 1)
# Simple Total/ClassCount doubled effect
elif weightType == "2x":
classWeights = 1 - (classCount / classCount.sum() * 2)
# Simple Total/ClassCount divided by Minimum
elif weightType == "division":
classWeights = classCount.sum() / classCount
#divide with minimum
classWeights[classWeights == 1] = 999999
classWeights /= classWeights.min()
# all weights are relative to the smallest class which is assigned the 1.0. Minimal assigned value is 0.1
elif weightType == "relativeToMin":
classWeights = classCount.min() / classCount
print("Class weights: ", classWeights.shape, classWeights)
classWeights[(classWeights < 0.1)] *= 10
# using the quantile transformer of sklearn all the weights are distributed in 0-0.9999. Minimal assigned value is 0.1
elif weightType == "quantile":
from sklearn.preprocessing.data import QuantileTransformer
_scaler = QuantileTransformer()
classCount = np.expand_dims(classCount, axis=1)
classWeights = _scaler.fit_transform(classCount)
classWeights = np.around(classWeights, decimals=4)
classWeights = np.squeeze(classWeights)
classWeights = 1 - classWeights
classWeights[(classWeights < 0.1)] = 0.1
else:
raise ValueError(
"Wrong weights calc type given! Valid arguments are [Freq, MedianFreq, 1x, 2x, division, relativeToMin, quantile]"
)
# eliminate inf values
classWeights[(classWeights == np.inf)] = 1
print("Class weights: ", classWeights.shape, classWeights)
np.save(
"classWeights" + str(self.config["x"]) + str(self.config["y"]) +
self.config["name"], classWeights)
# Feature 5 has a few but very large outliers.
X = X_full[:, [0, 5]]
distributions = [
('Unscaled data', X),
('Data after standard scaling',
StandardScaler().fit_transform(X)),
('Data after min-max scaling',
MinMaxScaler().fit_transform(X)),
('Data after max-abs scaling',
MaxAbsScaler().fit_transform(X)),
('Data after robust scaling',
RobustScaler(quantile_range=(25, 75)).fit_transform(X)),
('Data after quantile transformation (uniform pdf)',
QuantileTransformer(output_distribution='uniform')
.fit_transform(X)),
('Data after quantile transformation (gaussian pdf)',
QuantileTransformer(output_distribution='normal')
.fit_transform(X)),
('Data after sample-wise L2 normalizing',
Normalizer().fit_transform(X))
]
# scale the output between 0 and 1 for the colorbar
y = minmax_scale(y_full)
def create_axes(title, figsize=(16, 6)):
fig = plt.figure(figsize=figsize)
fig.suptitle(title)
for i1 in y1:
a1 = np.binary_repr(i1, 7)
ans1.append(a1)
df1['ans1'] = pd.DataFrame(ans1)
for i1 in range(7):
df['BS1' + str(i1)] = df1['ans1'].str[i1]
# remove original style name now
df.drop(['Style Name'], 1, inplace=True)
df = df.dropna(axis=0)
c = df.columns[df.dtypes.eq(object)]
df[c] = df[c].apply(pd.to_numeric, errors='coerce', axis=0)
scaler = QuantileTransformer()
#df3 = scaler.fit_transform(df)
X5 = np.array(df.drop(['Score'], 1))
y5 = np.array(df['Score'])
X3 = scaler.fit_transform(pd.DataFrame(X5))
#y3 = scaler.fit_transform(pd.DataFrame(y5))
y3 = 1.2 - np.log(y5)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X5, y5, test_size=0.20)
# Data Munging Compleated.######################
from keras import layers, models
class FeatureMap(object):
def __init__(self, df):
self.df = copy.deepcopy(df)
self.onehot = None
self.label_code = None
self.col_label_dict = dict()
self.min_max_scale = None
self.max_abs_scale = None
self.standard_scale = None
self.robust_scale = None
self.quantile_transform = None
def log_map(self, col_need, col_replace=True):
df_need = self.df[col_need]
if col_replace:
self.df[col_need] = df_need.apply(lambda x: np.log(x))
else:
col_need_extend = [col + "_log" for col in col_need]
self.df[col_need_extend] = df_need.apply(lambda x: np.log(x))
def box_cox_map(self, col_need, gamma=1.0, col_replace=True):
"""
y = ((1+x)**gamma - 1) / gamma if gamma != 0
log(1+x) if gamma == 0
ref: http://onlinestatbook.com/2/transformations/box-cox.html
:param col_need:
:param gamma:
:param col_replace:
:return:
"""
df_need = self.df[col_need]
if col_replace:
self.df[col_need] = df_need.applymap(lambda x: boxcox1p(x, gamma))
else:
col_need_extend = [col + "_boxCox" for col in col_need]
self.df[col_need_extend] = df_need.applymap(
lambda x: boxcox1p(x, gamma))
def onehot_encode(self, col_need, start_zero=True):
"""
onehot encode DataFrame of which the columns you need
note: the origin category should be integer in range(classes) or range(classes+1)
:param col_need:
:param start_zero: category is in range(classes)
:return: new DataFrame without col_need, after onehot encoding,
start method is in accordance with start_zero
"""
self.onehot = OneHotEncoder(sparse=False)
array_onehot = self.onehot.fit_transform(self.df.loc[:, col_need])
col_onehot = []
for col_index in range(len(col_need)):
if start_zero:
for hot_index in range(self.onehot.n_values_[col_index]):
col_onehot.append(col_need[col_index] + str(hot_index))
else:
for hot_index in range(1, self.onehot.n_values_[col_index]):
col_onehot.append(col_need[col_index] + str(hot_index))
self.df.drop(col_need, axis=1, inplace=True)
df_onehot = pd.DataFrame(array_onehot,
columns=col_onehot,
index=self.df.index)
self.df = pd.concat([self.df, df_onehot], axis=1)
def label_encode(self, col_need):
"""
onehot encode DataFrame of which the columns you need
:param col_need: length should be 1
:return: new DataFrame without col_need, after label encoding, start from 0
"""
assert isinstance(col_need, list) and len(col_need) == 1
self.label_code = LabelEncoder()
array_label_code = self.label_code.fit_transform(self.df.loc[:,
col_need])
label_list = list(self.label_code.classes_)
for i, x in enumerate(label_list):
self.col_label_dict[col_need[0] + "_" +
str(i)] = col_need[0] + "_" + x
self.df.drop(col_need, axis=1, inplace=True)
df_label_code = pd.DataFrame(array_label_code,
columns=col_need,
index=self.df.index)
self.df = pd.concat([self.df, df_label_code], axis=1)
def standard_scale_map(self, col_need, drop_origin_col=False):
self.standard_scale = StandardScaler()
array_standard = self.standard_scale.fit_transform(
self.df.loc[:, col_need])
self._scale_map(array=array_standard,
column_name=col_need,
suffix="_stdScale",
drop_origin_columns=drop_origin_col)
def min_max_scale_map(self, col_need, drop_origin_col=False):
self.min_max_scale = MinMaxScaler()
array_min_max = self.min_max_scale.fit_transform(self.df.loc[:,
col_need])
self._scale_map(array=array_min_max,
column_name=col_need,
suffix="_minMaxScale",
drop_origin_columns=drop_origin_col)
def max_abs_scale_map(self, col_need, drop_origin_col=False):
self.max_abs_scale = MaxAbsScaler()
array_max_abs = self.max_abs_scale.fit_transform(self.df.loc[:,
col_need])
self._scale_map(array=array_max_abs,
column_name=col_need,
suffix="_maxAbsScale",
drop_origin_columns=drop_origin_col)
def robust_scale_map(self,
col_need,
quantile_range=(25, 75),
drop_origin_col=False):
"""
This Scaler removes the median and scales the data according to
the quantile range (defaults to IQR: Interquartile Range).
The IQR is the range between the 1st quartile (25th quantile)
and the 3rd quartile (75th quantile).
:param col_need:
:param quantile_range:
:param drop_origin_col:
:return:
"""
self.robust_scale = RobustScaler(quantile_range=quantile_range)
array_robust = self.robust_scale.fit_transform(self.df.loc[:,
col_need])
self._scale_map(array=array_robust,
column_name=col_need,
suffix="_robust_scale",
drop_origin_columns=drop_origin_col)
def quantile_scale_map(self,
col_need,
distribution='uniform',
drop_origin_col=False):
"""
:param col_need:
:param distribution: 'uniform' (default) or 'normal'
:param drop_origin_col:
:return:
"""
self.quantile_transform = QuantileTransformer(
output_distribution=distribution)
array_quantile = self.quantile_transform.fit_transform(
self.df.loc[:, col_need])
self._scale_map(array=array_quantile,
column_name=col_need,
suffix="_q{}Map".format(distribution.capitalize()),
drop_origin_columns=drop_origin_col)
def _scale_map(self,
array,
column_name,
suffix,
drop_origin_columns=False):
if drop_origin_columns:
self.df.drop(column_name, axis=1, inplace=True)
col = [col + suffix for col in column_name]
df_scale = pd.DataFrame(array, columns=col, index=self.df.index)
self.df = pd.concat([self.df, df_scale], axis=1)
def quantile_floor_map(self, col_need, floor_num=5, drop_origin_col=False):
"""
after quantile_scale_map when distribution='uniform', value is scaled in [0, 1]
for tree models, onehot encoding is need
:param col_need:
:param floor_num: uniform floor map
:param drop_origin_col
:return:
"""
bool0 = (self.df.loc[:, col_need] >= 0) & (self.df.loc[:, col_need] <=
1)
assert bool0.all().all()
col_suffix = np.array([x.endswith("_qUniformMap") for x in col_need])
assert np.prod(col_suffix)
array_quantile_floor = (self.df.loc[:, col_need].values *
floor_num).astype(np.int)
self._scale_map(array=array_quantile_floor,
column_name=col_need,
suffix="_qFloorMap",
drop_origin_columns=drop_origin_col)
# on the distribution of values being transformed
# Documentation at https://seaborn.pydata.org/generated/seaborn.distplot.html
#this is the list of transformations to perform on the data
distributions = [
('Unscaled data', X, 'Unscaled'),
('Data after standard scaling',
StandardScaler().fit_transform(X), 'standard'),
('Data after min-max scaling',
MinMaxScaler().fit_transform(X), 'min_max'),
('Data after max-abs scaling',
MaxAbsScaler().fit_transform(X),'max_abs'),
('Data after robust scaling',
RobustScaler(quantile_range=(25, 75)).fit_transform(X),'robust'),
('Data after quantile transformation (uniform pdf)',
QuantileTransformer(output_distribution='uniform')
.fit_transform(X),'quantile'),
('Data after quantile transformation (gaussian pdf)',
QuantileTransformer(output_distribution='normal')
.fit_transform(X),'gaussian'),
('Data after sample-wise L2 normalizing',
Normalizer().fit_transform(X),'normalizing'),
('Data after Log + 1', np.log(X+1), 'Log + 1'),
]
#define function to produce the graphic
def create_plts(x):
temp = distributions[x][2]
temp, ax = plt.subplots()
sns.distplot(distributions[x][1]).set_title(distributions[x][0])
temp.savefig(cols + distributions[x][0] + '.pdf',
bbox_inches = 'tight', dpi=None, facecolor='w', edgecolor='b',
def __init__(self, nQuantile=None):
self.nQuantile = Separator_num.nQUANTILE if nQuantile is None else nQuantile
Pipeline.__init__(self, [ ('geometry' , Separator_num.Selector())
, ('quantiled', QuantileTransformer(n_quantiles=self.nQuantile, copy=False)) #use in-place scaling
])
return x
Scalers = {
'Unscaled data':
LinearScaler(),
'Standard scaling':
StandardScaler(),
'Min-max scaling':
MinMaxScaler(),
'Max-abs scaling':
MaxAbsScaler(),
'Robust scaling':
RobustScaler(quantile_range=(25, 75)),
'Quantile transformation (uniform pdf)':
QuantileTransformer(output_distribution='uniform'),
'Quantile transformation (gaussian pdf)':
QuantileTransformer(output_distribution='normal'),
'Sample-wise L2 normalizing':
Normalizer(norm='l2'),
'Sample-wise L1 normalizing':
Normalizer(norm='l1'),
}
def create_axes(title, figsize=(16, 6)):
fig = plt.figure(figsize=figsize)
fig.suptitle(title)
# define the axis for the first plot
left, width = 0.1, 0.22
# Author: Franz Weidmann
# Info: Creates for each hosts an SVM one class classifier to retrieve the normal
# state of the host. All models will be trained and save into a npy file
import numpy as np
from sklearn import svm
from sklearn.externals import joblib
from sklearn.preprocessing.data import QuantileTransformer
trainData = np.load("../../data/data.npy")
# transform data for scaling and save the state of the transformer for each host
scalers = []
for h in range(trainData.shape[0]):
_scaler = QuantileTransformer()
trainData[h] = _scaler.fit_transform(trainData[h])
scalers.append(_scaler)
# train and svm one class classifier for every host
models = []
for modelIndex in range(trainData.shape[0]):
print("Creating model ", modelIndex)
model = svm.OneClassSVM(kernel="rbf", verbose=True)
model.fit(trainData[modelIndex])
models.append(model)
print("Trained model ", modelIndex)
joblib.dump([scalers, models], "models.pkl")