def drop_low_var_columns(train_X, test_df, threshold):
select_features = feature_selection.VarianceThreshold(threshold=threshold)
select_features.fit(train_X)
cols_to_drop = train_X.columns[select_features.get_support() != True]
train_X.drop(columns=cols_to_drop, inplace=True)
test_df.drop(columns=cols_to_drop, inplace=True)
def feature_select_low_var(dataframe,
vars_name=None,
threshold_=1,
ret_normal=False):
'''
Feature selection using low variance method to the normalized applied features in dataframe.
Those features with variance below threshold are eliminated.
Parameters
----------
dataframe = features-only dataframe (response variable excluded)
vars_name = list/array of strings with each feature name
threshold = '1' default. Beware data is normalized so 'threshold = 1' is tantamount to 1x std deviations.
ret_normal = 'False' default. If 'False' dataframe returned is normalized, otherwise the original data is returned.
'''
# Dataframe column names automatic obtention unless provided:
if vars_name == None:
try:
names = dataframe.columns
except:
names = vars_name
else:
names = vars_name
# taking out non-numeric vars:
type_list = map(lambda x: type(x), dataframe.iloc[0, :])
type_idx = [i != str for i in type_list]
df = dataframe.iloc[:, type_idx]
# normalizing numeric variables to get rid of scale effect over variance:
df_t = preprocessing.StandardScaler().fit_transform(df)
# Low Variance variable selection:
selection = fs.VarianceThreshold(threshold=threshold_).fit_transform(df_t)
# Retrieve selected variable names:
sel_fit = fs.VarianceThreshold(threshold=threshold_).fit(df_t)
selec_vars = selec_vars = names[sel_fit.get_support()]
for i in selection[0, :]:
temp = i == df_t[0, :]
selec_vars.append(names[temp][0])
if ret_normal == True:
df_sel = pd.DataFrame(selection, columns=selec_vars)
print('Returned variables are normalized')
else:
df_sel = pd.DataFrame(dataframe[selec_vars], columns=selec_vars)
print('Returned variables are original')
return df_sel
def get_lovvar_cols(df, threshold):
selector = feature_selection.VarianceThreshold(threshold=threshold)
selector.fit(df)
cols = df.columns[~selector.get_support()]
cols = [col for col in cols if col not in ["Field9", "Field8", 'Field11', 'Field12']]
return cols
def variance_threshold(arr0, threshold):
matrix = np.array(arr0)
temp = feature_selection.VarianceThreshold(threshold=threshold).fit(matrix)
scores = [np.var(el) for el in matrix.T]
indx = temp.get_support().tolist()
# result = data_utility.retrieve_nan_index(temp.transform(matrix).tolist(), index)
result = temp.transform(matrix).tolist()
return scores, indx, result
def stand():
"""
归一化处理
:return: none
"""
value = [[10, 30, 80], [0, 0.5, 1], [1, 1, 2]]
std = feature_selection.VarianceThreshold(200)
print(std.fit_transform(value))
def removeZeroVarianceFeatures(X):
'''Function to remove features which use zero variance
Parameters: X (np.array): Features for the dataset
Return: X (np.array): Modified array of features
with no zero-variance features
'''
varianceSelector = feature_selection.VarianceThreshold()
X = varianceSelector.fit_transform(X)
return X
def get_fs_model(model, method, train, target=None, cv=None):
"""Connects given model with specified feature selection method and trains
the final structure.
"""
if method == "RFE":
model = fs_scikit.RFE(model, 2, step=5)
if target is not None:
return model.fit(train, target)
else:
return model.fit(train)
if method == "RFECV":
model = fs_scikit.RFECV(model, 3, cv=cv)
if target is not None:
return model.fit(train, target)
else:
return model.fit(train)
elif method == "linearSVC":
sel = SelectFromModel(LinearSVC(penalty='l1', dual=False))
model = Pipeline([('feature_selection', sel), ('data_mining', model)])
elif method == "fromModel":
fm = fs_scikit.SelectFromModel(model)
if target is not None:
fm.fit(train, target)
else:
fm.fit(train)
model = Pipeline([('feature_selection', fm), ('data_mining', model)])
# elif method == "Anova":
# ANOVA SVM-C
# anova_filter = fs_scikit.SelectKBest(f_regression, k=5)
# model = Pipeline([
# ('feature_selection', anova_filter),
# ('data_mining', model)
# ])
elif method == "VarianceThreshold":
sel = fs_scikit.VarianceThreshold(threshold=(.8 * (1 - .8)))
model = Pipeline([('feature_selection', sel), ('data_mining', model)])
elif method == "SelectPercentile":
sel = fs_scikit.SelectPercentile(fs_scikit.f_classif, percentile=30)
model = Pipeline([('feature_selection', sel), ('data_mining', model)])
elif method == "SelectFpr":
sel = fs_scikit.SelectFpr(alpha=0.2)
model = Pipeline([('feature_selection', sel), ('data_mining', model)])
elif method == "SelectFdr":
sel = fs_scikit.SelectFdr(alpha=0.2)
model = Pipeline([('feature_selection', sel), ('data_mining', model)])
elif method == "SelectFwe":
sel = fs_scikit.SelectFwe(alpha=0.2)
model = Pipeline([('feature_selection', sel), ('data_mining', model)])
elif method == "ch2":
sel = fs_scikit.SelectKBest(fs_scikit.chi2, k=2)
model = Pipeline([('feature_selection', sel), ('data_mining', model)])
else:
print("Feature selection method was not found: " + method)
sys.exit(1)
return model
def near_zero_var_df_sklearn(
self,
df: PandasDataFrame,
excludes: List,
file_name: str,
thresh_variance: float = 0.05,
to_search: bool = True
) -> [PandasDataFrame, CollectionsOrderedDict]:
"""Find and optionally remove the selected near-zero-variance features (Scikit algorithm).
Feature selector that removes all low-variance features.
This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be
used for unsupervised learning.
:param df: the features dataframe.
:param excludes: the name of excluded features.
:param file_name: the name of the summary output file.
:param thresh_variance: Features with a training-set variance lower than this threshold will be removed.
The default is to keep all features with non-zero variance, i.e. remove the features that have the same
value in all samples.
:param to_search: to search or use the saved configuration.
:return: the inputted dataframe with exclusion of features that were selected to be removed.
"""
self.__logger.debug(
"Remove features with near-zero-variance (if applicable), using Scikit algorithm."
)
df_excludes = df[excludes]
excludes = set(excludes)
matches = []
indices = OrderedDict()
summaries = OrderedDict()
# find indices
for label in df.columns.values():
indices[df.columns.get_loc(label)] = label
# search
if to_search is True:
variances_ = feature_selection.VarianceThreshold(thresh_variance)
matches_indices = variances_.get_support(indices=True)
matches_labels = [indices[index] for index in matches_indices]
for match in matches_labels:
if match not in excludes:
matches += [match]
# delete
df = self.__remove(
df, {'NZV': list(matches)}, to_search,
os.path.join(self.__output_path, file_name + ".ini"))
for name in excludes:
df[name] = df_excludes[name]
if any(np.isnan(df.index)):
df = df.reset_index(drop=True)
# summaries
if to_search is True:
summaries["Features Matches"] = matches
return df, summaries
def remove_low_variance(data):
#https://scikit-learn.org/stable/modules/feature_selection.html
#X = [[0, 0, 1], [0, 1, 0], [1, 0, 0], [0, 1, 1], [0, 1, 0], [0, 1, 1]]
#if features have low variance in 40% of samples, trim
sel = feature_selection.VarianceThreshold(threshold=(1 - 0.05) * 0.05)
new_dataset = sel.fit_transform(data)
selected_feature_indicies = sel.get_support(indices=True)
print(len(selected_feature_indicies))
print(selected_feature_indicies)
np.save("list of chosen features_05", selected_feature_indicies)
np.save("low_variance_dataset_alpha05_new", new_dataset)
return new_dataset
def lars():
behavior_data, conn_data = pu.load_data_full_subjects()
conn_data.astype(float)
categorical_variables = ['smoking', 'deanxit_antidepressants', 'rivotril_antianxiety', 'sex']
categorical_data = behavior_data[categorical_variables]
dummy_coded_categorical = pu.dummy_code_binary(categorical_data)
covariate_data = pd.concat([behavior_data['age'], dummy_coded_categorical], axis=1)
ml_data = pd.concat([conn_data, covariate_data], axis=1)
target = behavior_data['distress_TQ'].values.astype(float)
feature_names = list(ml_data)
continuous_features = [f for f in feature_names if 'categorical' not in f]
continuous_indices = [ml_data.columns.get_loc(cont) for cont in continuous_features]
categorical_features = [f for f in feature_names if 'categorical' in f]
categorical_indices = [ml_data.columns.get_loc(cat) for cat in categorical_features]
ml_continuous = ml_data.values[:, continuous_indices]
ml_categorical = ml_data.values[:, categorical_indices]
# Standardization for continuous data
preproc = preprocessing.StandardScaler().fit(ml_continuous)
ml_z = preproc.transform(ml_continuous)
# Variance threshold for categorical data
varthresh = feature_selection.VarianceThreshold(threshold=0).fit(ml_categorical)
ml_v = varthresh.transform(ml_categorical)
ml_preprocessed = np.hstack((ml_z, ml_v))
# Feature selection with extra trees
clf = ensemble.ExtraTreesRegressor()
model = feature_selection.SelectFromModel(clf, threshold="2*mean")
# Transform train and test data with feature selection model
ml_cleaned = model.fit_transform(ml_preprocessed, target)
feature_indices = model.get_support(indices=True)
cleaned_features = [feature_names[i] for i in feature_indices]
lars_classifier = linear_model.LarsCV(cv=3, normalize=False, fit_intercept=False)
lars_classifier.fit(ml_cleaned, target)
predicted = lars_classifier.predict(ml_cleaned)
r2 = lars_classifier.score(ml_cleaned, target)
exp_var = metrics.explained_variance_score(target, predicted)
max_err = metrics.max_error(target, predicted)
mae = metrics.mean_absolute_error(target, predicted)
mse = metrics.mean_squared_error(target, predicted)
print(r2)
def variance_threshold(data, alpha=0.05):
"""A wrapper of scikit-learn VarianceThreshold."""
X_train, X_test, y_train, y_test = data
# Z-scores.
X_train_std, X_test_std = utils.train_test_z_scores(X_train, X_test)
selector = feature_selection.VarianceThreshold(threshold=alpha)
# NB: Cannot filter variance from standardized data.
selector.fit(X_train, y_train)
support = _check_support(selector.get_support(indices=True), X_train_std)
return _check_feature_subset(X_train_std, X_test_std, support)
def key_features(X_train, y_train, sub, varience_test=True):
print 'Features before reduction: ' + str(len(X_train[0]))
if varience_test:
#remove features with low variance
sel = feature_selection.VarianceThreshold(threshold=(.8 * (1 - .8)))
X_train = sel.fit_transform(X_train)
sub = sel.transform(sub)
print 'Features after variance reduction: ' + str(len(X_train[0]))
estimator = linear_model.SGDClassifier(n_jobs=-1, class_weight='auto')
selector = feature_selection.RFECV(estimator, step=1, cv=5)
features = selector.fit_transform(X_train, y_train)
submission = selector.transform(sub)
print 'Features after recursive elimination: ' + str(len(features[0]))
return (features, submission)
def clean_features(data, header, **kwargs):
#extract parameters
min_feature_variance = kwargs.get('min_feature_variance', .8 * (1 - .8))
#remove features with variance below the threshold
feature_selector = feature_selection.VarianceThreshold(
threshold=min_feature_variance)
reduced_data = feature_selector.fit_transform(data)
#create a mask of features selected
mask = feature_selector.get_support(indices=True)
#select the same indexes from the header
reduced_header = np.take(header, mask)
return reduced_data, reduced_header
def learning(X, y, estimator, search_params):
scoring = dict(roc_auc='roc_auc',
pr_auc=metrics.make_scorer(pr_auc_score),
accuracy='accuracy',
balanced_accuracy='balanced_accuracy',
precision='precision',
recall='recall',
f1='f1',
mcc=metrics.make_scorer(metrics.matthews_corrcoef))
pipe = Pipeline([
('var', feature_selection.VarianceThreshold(
threshold=0)), # To remove constant features
('bal',
None), # To balance the training data See search_params['bal'] below)
('pre',
None), # To scale (and center) data. See search_params['pre'] below
('clf', estimator)
])
groups = X.group.tolist()
X = X.drop(['group'], axis=1)
search_params['bal'] = [
None,
RandomUnderSampler(sampling_strategy='majority', random_state=42),
RandomOverSampler(sampling_strategy='minority', random_state=42)
]
search_params['pre'] = [
None,
preprocessing.MinMaxScaler(),
preprocessing.StandardScaler()
]
search = model_selection.RandomizedSearchCV(pipe,
search_params,
cv=release_split(X, y, groups),
scoring=scoring,
refit='pr_auc',
verbose=0)
search.fit(X, y)
return search.cv_results_, search.best_index_
def fit_transform(self, data):
"""Fit and transform using feature filtering.
Fit and transform using several kind of feature filtering methods to
select features in data.
:param data: Dataframe. The Pandas dataframe, to be converted.
:return: Dataframe. The converted dataframe after feature filtering.
"""
# Removing features with low variance.
threshold = 0.0
var_thre = fe.VarianceThreshold(threshold=threshold)
result = var_thre.fit_transform(data[data.columns.difference(
[self.target_column])])
feature_select = data.columns.difference([self.target_column
])[var_thre.get_support()]
result = pd.DataFrame(columns=feature_select, data=result)
result[self.target_column] = data[self.target_column]
# Store converter.
self.variance_threshold = var_thre
# Univariate feature selection, using univariate statistical tests.
data = result
univar_select = fe.GenericUnivariateSelect(
score_func=fe.mutual_info_classif, mode='fwe', param=0.05)
# Check whether it's regression or classification.
# If classification, skip univariate.
if len(data[self.target_column].value_counts()) <= 2:
return result
# If Regression.
result = univar_select.fit_transform(
data[data.columns.difference([self.target_column])],
np.asarray(data[self.target_column]))
feature_select = data.columns.difference(
[self.target_column])[univar_select.get_support()]
result = pd.DataFrame(columns=feature_select, data=result)
result[self.target_column] = data[self.target_column]
# Store converter.
self.univar_select = univar_select
return result
def get_low_var_cols(df, threshold):
"""Analyse a Pandas DataFrame, extract the numeric columns and
return a list of those which have a variance below the threshold.
Args:
:param df: Pandas DataFrame.
:param threshold: Variance threshold.
Returns:
List of columns with variance below the threshold.
Example:
low_var_cols = get_low_var_cols(df, 0.01)
"""
df = df.select_dtypes(['number'])
selector = feature_selection.VarianceThreshold(threshold=threshold)
selector.fit(df)
return df.columns[~selector.get_support()]
def fitness(data, metric='euclidean', k=10, seed=None):
if metric == 'variance':
sel = fs.VarianceThreshold()
sel.fit(data)
return np.average(np.array(sel.variances_))
if seed < 0:
random_seed = None
else:
random_seed = seed
km = KMeans(n_clusters=k, random_state=random_seed, n_jobs=-1)
labels = km.fit_predict(data)
if metric == 'euclidean':
return silhouette_score(data, labels)
elif metric == 'cosine':
return silhouette_score(data, labels, metric='cosine')
else:
return km.inertia_
def get_features(X, y, fsm):
if fsm == '1':
# SelectKBest
kBest = math.ceil(len(X[0]) / 2)
feature_scores = f_selection.SelectKBest(chi2,
k=kBest).fit_transform(X, y)
return feature_scores
elif fsm == '2':
# VarianceThreshold
feature_scores = f_selection.VarianceThreshold(
threshold=vThreshold).fit_transform(X)
return feature_scores
elif fsm == '3':
# SelectFromModel
clf = ExtraTreesClassifier(random_state=200)
clf = clf.fit(X, y)
model = f_selection.SelectFromModel(clf).fit(X, y)
feature_scores = model.transform(X)
return feature_scores
else:
raise ValueError("invalid fsm")
def get_model(self, resume=False):
if not resume:
if self.method == 'variance': # Unsupervised
p = .5
selector = feature_selection.VarianceThreshold(
threshold=(p * (1 - p)))
elif self.method == 'rfe':
estimator = LogisticRegression()
selector = feature_selection.RFE(
estimator,
n_features_to_select=self.feat_limit,
step=1,
verbose=0)
elif self.method == 'forward':
estimator = ExtraTreesClassifier(n_estimators=100)
selector = SelectFromModel(estimator)
elif self.method == 'seq_bwd':
estimator = LogisticRegression(solver='lbfgs')
selector = SFS(estimator,
k_features=self.feat_limit,
forward=False,
floating=False,
scoring='roc_auc',
cv=4,
n_jobs=-1)
elif self.method == 'seq_fwd':
estimator = LogisticRegression(solver='lbfgs')
selector = SFS(estimator,
k_features=self.feat_limit,
forward=True,
floating=False,
scoring='roc_auc',
cv=4,
n_jobs=-1)
else:
selector = joblib.load(self.model_save_path)
if self.verbose > 2:
print(selector)
return selector
def train(self, features):
# Setup:
start_time = time.time()
# Check feature set:
assert (np.isfinite(features).all())
# Normalizer:
if self.normalize:
standardizer = preprocessing.StandardScaler()
features = standardizer.fit_transform(features)
self.normalizer = standardizer
# Option 1 (Random Projection):
if self.reducer_type == Reducers.random_projection:
transformer = random_projection.GaussianRandomProjection()
transformer.fit(features)
self.reducer = transformer
# Option 2 (Feature Selection):
if self.reducer_type == Reducers.feature_selection:
threshold = (self.explained_variance) * (1 -
self.explained_variance)
selector = feature_selection.VarianceThreshold(threshold=threshold)
selector.fit(features)
self.reducer = selector
# Option 3 (PCA):
if self.reducer_type == Reducers.pca:
pca = decomposition.PCA(n_components=self.explained_variance,
svd_solver="full")
pca.fit(features)
self.reducer = pca
# Calculate elapsed time:
end_time = time.time()
elapsed_time = end_time - start_time
print("Training preprocessor took %.2f seconds" % elapsed_time)
def pre_process(X, X_pred, mode='full'):
preprocessings = {
'standards':preprocessing.StandardScaler(),
'minmaxs':preprocessing.MinMaxScaler(),
'robusts':preprocessing.RobustScaler(),
'PCA': PCA(),
'PowerTransformer':preprocessing.Normalizer(),
'variance_threshold':feature_selection.VarianceThreshold(threshold=0.5),
}
if mode == 'full':
pipe_preprocessing = make_pipeline(
# preprocessings['variance_threshold'],
preprocessings['standards'],
<<<<<<< HEAD
preprocessings['robusts'],
preprocessings['PCA'],
preprocessings['PowerTransformer'],
preprocessings['minmaxs'],
=======
# preprocessings['minmaxs'],
preprocessings['robusts'],
# preprocessings['PCA'],
preprocessings['PowerTransformer'],
>>>>>>> bb38b4fdedf3f7cc3dab38fea065353aeef512fa
)
elif mode == 'min_max_scale':
pipe_preprocessing = make_pipeline(
preprocessings['minmaxs'],
preprocessings['PCA'],
)
full = np.concatenate((X, X_pred), axis=0)
pipe_preprocessing.fit(full)
X = pipe_preprocessing.transform(X)
X_pred = pipe_preprocessing.transform(X_pred)
return X, X_pred
def feature_selection_with_covariates(x_train, x_test, y_train,
continuous_indices, categorical_indices,
feature_names):
# Split data for continuous, categorical preprocessing
x_train_cont, x_test_cont = x_train[:,
continuous_indices], x_test[:,
continuous_indices]
x_train_cat, x_test_cat = x_train[:,
categorical_indices], x_test[:,
categorical_indices]
# Standardization for continuous data
preproc = preprocessing.StandardScaler().fit(x_train_cont)
x_train_z = preproc.transform(x_train_cont)
x_test_z = preproc.transform(x_test_cont)
# Variance threshold for categorical data
varthresh = feature_selection.VarianceThreshold(
threshold=0).fit(x_train_cat)
x_train_v = varthresh.transform(x_train_cat)
x_test_v = varthresh.transform(x_test_cat)
x_train_data = np.hstack((x_train_z, x_train_v))
x_test_data = np.hstack((x_test_z, x_test_v))
# Feature selection with extra trees
extra_tree_fs = ensemble.ExtraTreesClassifier(random_state=seed)
feature_model = feature_selection.SelectFromModel(extra_tree_fs,
threshold="2*mean")
# Transform train and test data with feature selection model
x_train_feature_selected = feature_model.fit_transform(
x_train_data, y_train)
x_test_feature_selected = feature_model.transform(x_test_data)
feature_indices = feature_model.get_support(indices=True)
cleaned_features = [feature_names[i] for i in feature_indices]
return x_train_feature_selected, x_test_feature_selected, cleaned_features
def clean_features(data, name):
df = data[name]
X_columns = df.columns
# remove stellar classes
flt = ~(("nbg" == X_columns) | X_columns.str.endswith("_id")
| X_columns.str.contains("_scls_")
| X_columns.str.endswith("AndersonDarling")
| X_columns.str.endswith("StetsonJ")
| X_columns.str.endswith("StetsonK"))
X_columns = X_columns[flt]
X_columns
# remove signatures
X_columns = X_columns[~X_columns.str.startswith("Signature_")]
X_columns
# columns with nan and null
with_nulls = set()
for df in data.values():
for c in X_columns:
if df[c].isnull().any():
with_nulls.add(c)
print("Removing {} because null".format(list(with_nulls)))
X_columns = X_columns[~X_columns.isin(with_nulls)]
# low variance
df = pd.concat(data.values())
y = df["nbg"].values
vt = fs.VarianceThreshold()
vt.fit(df[X_columns].values, y)
print("Removing {} because lowvariance".format(
list(X_columns[~vt.get_support()])))
X_columns = X_columns[vt.get_support()]
return X_columns
def fitness(self):
data_projected = self.data[self.attributes]
if self.metric == 'variance':
sel = fs.VarianceThreshold()
sel.fit(data_projected)
return np.average(np.array(
sel.variances_)) - np.log(1 + self.violations)
km = KMeans(n_clusters=self.k,
random_state=self.random_seed,
n_jobs=-1)
labels = km.fit_predict(data_projected)
if self.metric == 'euclidean':
return silhouette_score(data_projected,
labels) - np.log(1 + self.violations)
elif self.metric == 'cosine':
return silhouette_score(
data_projected, labels,
metric=self.metric) - np.log(1 + self.violations)
else:
inertia = km.inertia_
order = self.getOrder(inertia)
return inertia + self.violations * order * 10**2
import scipy.stats as ss
import math
%matplotlib inline
Features = np.array(pd.read_csv('Credit_Features.csv'))
Labels = np.array(pd.read_csv('Credit_Labels.csv'))
print(Features.shape)
print(Labels.shape)
## -->> Eliminate low variance features
# VarianceThreshold function
print(Features.shape)
## Define the variance threhold and fit the threshold to the feature array.
sel = fs.VarianceThreshold(threshold=(.8 * (1 - .8)))
Features_reduced = sel.fit_transform(Features)
## Print the support and shape for the transformed features
print(sel.get_support())
print(Features_reduced.shape)
## -->>Select k best features
# RFECV function
## Reshape the Label array
Labels = Labels.reshape(Labels.shape[0],)
## Set folds for nested cross validation
nr.seed(988)
feature_folds = ms.KFold(n_splits=10, shuffle = True)
def get_zero_variance_filter(X_train):
tmp = feature_selection.VarianceThreshold()
return tmp.fit()
# 特征变换
for i in range(len(names_without_sex)):
data[names_without_sex[i]+'_log1p']=data[names_without_sex[i]].map(np.log1p)
data[names_without_sex[i]+'_sqrt']=data[names_without_sex[i]].map(np.sqrt)
for j in range(i+1, len(names_without_sex)):
data[names_without_sex[i]+'*'+names_without_sex[j]]=data[names_without_sex[i]]*data[names_without_sex[j]]
del i,j
# 特征选择
names=data.columns.drop(['id', '血糖'])
#划分训练集和测试集
train_xs=data.loc[data['血糖'] != 'unknown', names]
train_ys=data.loc[data['血糖'] != 'unknown', '血糖']
test_x =data.loc[data['血糖'] == 'unknown', names]
test_y =data.loc[data['血糖'] == 'unknown', ['id', '血糖']]
#特征选择:方差选择
VarianceThreshold=feature_selection.VarianceThreshold(threshold=0.1).fit(train_xs)
train_xs=VarianceThreshold.transform(train_xs)
test_x =VarianceThreshold.transform(test_x)
#特征选择:SelectPercentile
SelectPercentile=feature_selection.SelectPercentile(feature_selection.f_regression, percentile=50).fit(
train_xs, train_ys.map(np.float64))
train_xs=SelectPercentile.transform(train_xs)
test_x =SelectPercentile.transform(test_x)
# 降维
PCA=decomposition.PCA(n_components=50).fit(train_xs)
train_xs=PCA.transform(train_xs)
test_x =PCA.transform(test_x)
#标准化
scaler=preprocessing.StandardScaler().fit(train_xs)
train_xs=scaler.transform(train_xs)
test_x =scaler.transform(test_x)
def get_variances(self):
sel = fs.VarianceThreshold()
sel.fit(self.data)
return zip(self.attributes, sel.variances_)
dir = 'E:/10.kaggle(dont-overfit2)'
train = pd.read_csv(os.path.join(dir, 'train.csv'))
print(train.info())
print(train.columns)
sns.countplot(x='target',data=train)
#filter unique value features
train1 = train.iloc[:,2:]
y = train['target'].astype(int)
X_train, X_eval, y_train, y_eval = model_selection.train_test_split(train1, y, test_size=0.1, random_state=1)
stages = [ ('imputer', preprocessing.Imputer()),
('zv_filter', feature_selection.VarianceThreshold()),
('feature_selector', feature_selection.SelectKBest(score_func=feature_selection.f_classif)),
('classifier', linear_model.LogisticRegression())
]
pipeline_ml = pipeline.Pipeline(stages)
pipeline_grid = {'feature_selector__k':[70, 75, 100], 'classifier__C':[0.001, 0.01, 0.1, 0.2, 0.5],'classifier__penalty':['l1', 'l2'], 'classifier__class_weight':['balanced', None]}
pipeline_generated = cutils.grid_search_best_model(pipeline_ml, pipeline_grid, X_train, y_train, scoring="roc_auc")
final_estimator = pipeline_generated.named_steps['classifier']
print(pipeline_generated.score(X_eval, y_eval))
test = pd.read_csv(os.path.join(dir, 'test.csv'))
print(test.info())
print(test.columns)
test1 = test.iloc[:,1:]
test['target'] = np.round(pipeline_generated.predict_proba(test1)[:,1], 2)
print('Loading Trainset')
DATA_FOLDER = '../../../data/'
RES_FOLDER = '../../../results/base_features/'
train_x, gene_names_x, cell_names_x = dl.load_data(DATA_FOLDER +
'train_data.csv.gz')
# TODO: FIX DATA_LOAD FUNCTION SUCH THAT THIS ISN'T NECESSARY
train_x = train_x[:, 1:]
gene_names_x = gene_names_x[1:]
cell_names_x = cell_names_x[1:]
train_y, cell_names_y = dl.load_response(DATA_FOLDER + 'response.csv.gz')
# Preprocessing
varThresh = fsel.VarianceThreshold(threshold=0.1).fit(train_x)
scaled_x = prep.scale(varThresh.transform(train_x))
print('Loading Herring 2017')
herring_x, gene_names_herring, cell_names_herring = dl.load_data(
DATA_FOLDER + 'herring2017_data.csv.gz')
# TODO: FIX DATA_LOAD FUNCTION SUCH THAT THIS ISN'T NECESSARY
herring_x = herring_x[:, 1:]
gene_names_herring = gene_names_herring[1:]
herring_scaled = prep.scale(varThresh.transform(herring_x))
# Load Joost 2016 Data
print('Loading Joost 2016')
joost_x, gene_names_joost, cell_names_joost = dl.load_data(
DATA_FOLDER + 'joost2016_data.csv.gz')
# TODO: FIX DATA_LOAD FUNCTION SUCH THAT THIS ISN'T NECESSARY