from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import GridSearchCV, KFold, cross_val_predict, cross_val_score, StratifiedKFold
from sklearn.feature_selection import SelectKBest, SelectPercentile
from sklearn.feature_selection import f_classif, mutual_info_classif
from sklearn.compose import TransformedTargetRegressor
import load_data_ST
import save_output
import GSCV
name_clf = 'SVMR_linear_STDS'
#load data
data_train, labels_train, data_test, labels_test = load_data_ST.function_load_data_ST(
)
#Scalers
from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler
scalers_to_test = [StandardScaler(), RobustScaler(), MinMaxScaler()]
df = pd.DataFrame()
#Designate distributions to sample hyperparameters from
C_range = np.power(2, np.arange(-10, 8, dtype=float))
n_features_to_test = [0.85, 0.9, 0.95]
clf = TransformedTargetRegressor(regressor=SVR(kernel='linear'),
transformer=MinMaxScaler())
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.compose import TransformedTargetRegressor
from sklearn.linear_model import LinearRegression
import load_data_ST
import save_output
import nested_cv_ST
name_clf = 'LinearRegression'
#load data
data, labels = load_data_ST.function_load_data_ST()
#Scalers
from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler
scalers_to_test = [StandardScaler(), RobustScaler(), MinMaxScaler(), None]
df = pd.DataFrame()
# Designate distributions to sample hyperparameters from
n_features_to_test = [0.85, 0.9, 0.95]
clf = TransformedTargetRegressor(regressor=LinearRegression(),
transformer=MinMaxScaler())
#LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
import load_data_ST
import os
regr_RF_name = 'RandomForestRegressor'
regr_svml_name = 'SVR linear'
regr_rbf_name = 'SVR rbf'
regr_sig_name = 'SVR sigmoid'
#load data
pu_data, pu_labels, pa_data, pa_labels = load_data_ST.function_load_data_ST()
regr_RF = RandomForestRegressor(n_estimators=100,
max_depth=10,
criterion='mae',
random_state=503)
regr_svml = LinearRegression()
regr_rbf = SVR(kernel='rbf', C=0.25, gamma=0.0078125)
regr_sig = SVR(kernel='sigmoid', C=0.0625, gamma=125)
clf_RF = TransformedTargetRegressor(regressor=regr_RF,
transformer=MinMaxScaler())
from sklearn.compose import TransformedTargetRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler from sklearn.preprocessing import LabelEncoder from sklearn.metrics import roc_auc_score, classification_report, accuracy_score, balanced_accuracy_score import seaborn as sns import load_data_ST import os name_clf = 'RandomForestRegressor' #load data pu_data, pu_labels, PA_data, PA_labels = load_data_ST.function_load_data_ST() # Designate distributions to sample hyperparameters from n_features_to_test = [0.85, 0.9, 0.95] n_tree = [15, 30, 45, 60, 75, 90, 105, 120, 135, 150, 165, 180] depth = [ 2, 5, 10, 15, 20, 30, 45, 60, 75] regr_RF = RandomForestRegressor(criterion='mae', random_state=503) pca = PCA(random_state=42, n_components=0.85) #clf