# -*- coding: utf-8 -*-
#%%
from framework.model_stacking import ModelTrainer, ModelPerformanceTracker
from sklearn.neural_network import MLPClassifier as ThisModel
#%%
#
# Set up model for training
#
this_model = ModelTrainer(
ModelClass=ThisModel, #Model algorithm
model_params=dict(hidden_layer_sizes=(50, 25, 10)), #hyper-parameters
model_id='L1NN1', # Model Identifier
feature_set='L1FS01' # feature set to use
)
model_tracker = ModelPerformanceTracker(model_trainer=this_model)
#%%
#
# clear out old results
#
this_model.cleanPriorResults()
#%%
#
# train model on all the data
#
this_model.trainModel()
#%%
# create Test predictions
# -*- coding: utf-8 -*-
#%%
from framework.model_stacking import ModelTrainer, ModelPerformanceTracker
from sklearn.ensemble import RandomForestClassifier as ThisModel
#%%
#
# Set up model for training
#
this_model = ModelTrainer(
ModelClass=ThisModel, #Model algorithm
model_params=dict(n_estimators=200, max_depth=5,
n_jobs=-1), #hyper-parameters
model_id='L1RF1', # Model Identifier
feature_set='L1FS01' # feature set to use
)
model_tracker = ModelPerformanceTracker(model_trainer=this_model)
#%%
#
# clear out old results
#
this_model.cleanPriorResults()
#%%
#
# train model on all the data
#
this_model.trainModel()
#%%
# -*- coding: utf-8 -*-
#%%
from framework.model_stacking import ModelTrainer, ModelPerformanceTracker
from sklearn.linear_model import LogisticRegression as ThisModel
#%%
#
# Set up model for training
#
this_model = ModelTrainer(
ModelClass=ThisModel, #Model algorithm
model_params=dict(penalty='l1', C=0.1, tol=1e-5,
random_state=13), #hyper-parameters
model_id='L0LOG1', # Model Identifier
feature_set='KFS04' # feature set to use
)
model_tracker = ModelPerformanceTracker(model_trainer=this_model)
#%%
#
# clear out old results
#
this_model.cleanPriorResults()
#%%
#
# train model on all the data
#
this_model.trainModel()
#%%
# -*- coding: utf-8 -*-
#%%
from framework.model_stacking import ModelTrainer, ModelPerformanceTracker
from xgboost import XGBClassifier as ThisModel
#%%
#
# Set up model for training
#
this_model = ModelTrainer(
ModelClass=ThisModel, #Model algorithm
model_params=dict(n_estimators=100,n_jobs=6), #hyper-parameters
model_id='L0XGB1', # Model Identifier
feature_set='KFSBSLN' # feature set to use
)
model_tracker = ModelPerformanceTracker(model_trainer=this_model)
#%%
#
# clear out old results
#
this_model.cleanPriorResults()
#%%
#
# train model on all the data
#
this_model.trainModel()
#%%
# create Test predictions