from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
import load_data
import save_output
import nested_cv
name_1 = 'svm_lin_MMS'
name_2 = 'svm_lin_RBTS'
name_3 = 'svm_lin_STDS'
dim_reduction = 'PCA'
#load data
public_data, public_labels = load_data.function_load_data()
#Scalers
from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler
scalers_to_test = [RobustScaler(), MinMaxScaler()]
#Designate distributions to sample hyperparameters from
C_range = np.power(2, np.arange(-10, 11, dtype=float))
n_features_to_test = [0.85, 0.9, 0.95]
#SVM
steps = [('scaler', MinMaxScaler()), ('red_dim', PCA()), ('clf', SVC(kernel='linear', probability=True, random_state=503))]
pipeline = Pipeline(steps)
from sklearn.compose import TransformedTargetRegressor
from sklearn.linear_model import LinearRegression
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
from sklearn.model_selection import learning_curve
import plot_learning_curve
import load_data
import os
name_clf = 'LinearRegression'
#load data
data, labels = load_data.function_load_data()
outer_kf = KFold(n_splits=5, shuffle=True, random_state=2)
#clf
pca = PCA(random_state=42)
regr_svml = LinearRegression()
clf = TransformedTargetRegressor(regressor=regr_svml,
transformer=MinMaxScaler())
steps = [('scaler', StandardScaler()), ('red_dim', None), ('clf', clf)]
pipeline = Pipeline(steps)