forked from ATOMGP/ATOM
/
manager.py
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/
manager.py
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from learner import *
from optimizer import HyperoptOptimizer
import pandas as pd
import numpy as np
from kfold import *
from FeatureProcessor import FeatureProcessor
from metric import *
from ensembleSelection import *
from sklearn.preprocessing import LabelEncoder
import os
import time
from finalModel import finalModelESClassifier, finalModelStackingClassifier, finalModelESRegressor, finalModelStackingRegressor
from shared import Shared
from utils import *
from report_generator import *
from Stacking import Stacking
from shared import *
class Manager:
def __init__(self, pref_dict):
self._initialize_variables()
self.set_pref(pref_dict)
self.read_df()
self._set_target_vector()
def _initialize_variables(self):
self.project_dir = None
self.project_name = None
self.path = None
self.one_dataset = None
self.inverse_kfold = None
self.n_folds = None
self.train_data_path = None
self.test_data_path = None
self.target_variable_name = None
self.regression = None
self.pair_wise_elimination = None
self.two_way_features = None
self.three_way_features = None
self.svd_features = None
self.learners = []
self.n_evals = {}
self.total_evals = 0
self.metric = None
self.verbose = None
self.run_ensemble = None
self.generate_final_model = None
self.n_threads = None
self.train_df = None
self.X = None
self.test_df = None
self.X_test = None
self.Y = None
self.n_classes = None
self.kf = None
self.generate_test_prediction = False
self.history_df = None
self.best_df = pd.DataFrame(columns = ["Learner", "Score Mean", "Score Std", "Parameters", "Path"])
self.X_p = None
self.label_encoder = LabelEncoder()
self.feature_processor = FeatureProcessor()
self.ensemble_selection = None
self.stacking = None
def set_pref(self, pref_dict):
#project information
self.project_dir = pref_dict['projectLoc']
self.project_name = pref_dict['projectName']
if not os.path.isdir(self.project_dir + '/' +self.project_name):
os.mkdir(self.project_dir + '/' +self.project_name)
self.path = self.project_dir + '/' + self.project_name + '/'
self.one_dataset = pref_dict['oneDataset']
self.inverse_kfold = pref_dict['inverse_kfold']
self.n_folds = pref_dict['n_folds']
self.train_data_path = pref_dict['trainPath']
self.test_data_path = pref_dict['testPath']
self.target_variable_name = pref_dict['target']
self.regression = pref_dict['regression']
self.pair_wise_elimination = pref_dict['pwe']
self.two_way_features = pref_dict['twoWay']
self.three_way_features = pref_dict['threeWay']
self.svd_features = pref_dict['svd']
if 'LR' in pref_dict.keys() and pref_dict['LR']:
if not self.regression:
self.learners.append(LogisticRegression)
self.n_evals[LogisticRegression.__name__] = pref_dict['numEvalLR']
else:
self.learners.append(LassoRegression)
self.n_evals[LassoRegression.__name__] = pref_dict['numEvalLR']
self.learners.append(RidgeRegression)
self.n_evals[RidgeRegression.__name__] = pref_dict['numEvalLR']
self.total_evals += pref_dict['numEvalLR']
if 'KNN' in pref_dict.keys() and pref_dict['KNN']:
if not self.regression:
self.learners.append(KNeighborsClassifier)
self.n_evals[KNeighborsClassifier.__name__] = pref_dict['numEvalKNN']
else:
self.learners.append(KNeighborsRegressor)
self.n_evals[KNeighborsRegressor.__name__] = pref_dict['numEvalKNN']
self.total_evals += pref_dict['numEvalKNN']
if 'NB' in pref_dict.keys() and pref_dict['NB']:
if not self.regression:
#self.learners.append(MultinomialNB)
#self.n_evals[MultinomialNB.__name__] = pref_dict['numEvalNB']
self.learners.append(BernoulliNB)
self.n_evals[BernoulliNB.__name__] = pref_dict['numEvalNB']
self.total_evals += pref_dict['numEvalNB']
if 'GbTree' in pref_dict.keys() and pref_dict['GbTree']:
if not self.regression:
self.learners.append(XGBTreeClassifier)
self.n_evals[XGBTreeClassifier.__name__] = pref_dict['numEvalGbTree']
else:
self.learners.append(XGBTreeRegressor)
self.n_evals[XGBTreeRegressor.__name__] = pref_dict['numEvalGbTree']
self.total_evals += pref_dict['numEvalGbTree']
if 'GbLinear' in pref_dict.keys() and pref_dict['GbLinear']:
if not self.regression:
self.learners.append(XGBLinearClassifier)
self.n_evals[XGBLinearClassifier.__name__] = pref_dict['numEvalGbLinear']
else:
self.learners.append(XGBLinearRegressor)
self.n_evals[XGBLinearRegressor.__name__] = pref_dict['numEvalGbLinear']
self.total_evals += pref_dict['numEvalGbLinear']
if 'RBFSVM' in pref_dict.keys() and pref_dict['RBFSVM']:
if not self.regression:
self.learners.append(RBF_SVC)
self.n_evals[RBF_SVC.__name__] = pref_dict['numEvalRBFSVM']
else:
self.learners.append(RBF_SVR)
self.n_evals[RBF_SVR.__name__] = pref_dict['numEvalRBFSVM']
self.total_evals += pref_dict['numEvalRBFSVM']
if 'PolySVM' in pref_dict.keys() and pref_dict['PolySVM']:
if not self.regression:
self.learners.append(Poly_SVC)
self.n_evals[Poly_SVC.__name__] = pref_dict['numEvalPolySVM']
else:
self.learners.append(Poly_SVR)
self.n_evals[Poly_SVR.__name__] = pref_dict['numEvalPolySVM']
self.total_evals += pref_dict['numEvalPolySVM']
if 'LinearSVM' in pref_dict.keys() and pref_dict['LinearSVM']:
if not self.regression:
self.learners.append(Linear_SVC)
self.n_evals[Linear_SVC.__name__] = pref_dict['numEvalLinearSVM']
else:
self.learners.append(Linear_SVR)
self.n_evals[Linear_SVR.__name__] = pref_dict['numEvalLinearSVM']
self.total_evals += pref_dict['numEvalLinearSVM']
if 'ERT' in pref_dict.keys() and pref_dict['ERT']:
if not self.regression:
self.learners.append(ExtraTreesClassifier)
self.n_evals[ExtraTreesClassifier.__name__] = pref_dict['numEvalERT']
else:
self.learners.append(ExtraTreesRegressor)
self.n_evals[ExtraTreesRegressor.__name__] = pref_dict['numEvalERT']
self.total_evals += pref_dict['numEvalERT']
if 'RF' in pref_dict.keys() and pref_dict['RF']:
if not self.regression:
self.learners.append(RandomForestClassifier)
self.n_evals[RandomForestClassifier.__name__] = pref_dict['numEvalRF']
else:
self.learners.append(RandomForestRegressor)
self.n_evals[RandomForestRegressor.__name__] = pref_dict['numEvalRF']
self.total_evals += pref_dict['numEvalRF']
if pref_dict['MLP']:
self.learners.append(NeuralNetworkClassifier)
self.n_evals[NeuralNetworkClassifier.__name__] = pref_dict['numEvalMLP']
#else:
# self.learners.append(MLPRegressor)
# self.n_evals[MLPRegressor.__name__] = pref_dict['numEvalMLP']
self.run_ensemble = pref_dict['ensemble']
self.generate_final_model = pref_dict['generate_final_mode']
self.n_threads = pref_dict['numThread']
self.metric = eval(pref_dict['metric'])()
self.verbose = pref_dict['verbose']
def _set_target_vector(self):
if not self.regression:
self.Y = self.label_encoder.fit_transform(self.train_df[self.target_variable_name].values).flatten()
self.n_classes = np.unique(self.Y).shape[0]
else:
self.Y = self.train_df[self.target_variable_name].values.flatten()
self.train_df.drop([self.target_variable_name], axis=1, inplace=True)
if self.inverse_kfold:
self.kf = InverseKFold(self.n_folds, self.Y)
else:
self.kf = StratifiedKFold(self.Y, self.n_folds)
def read_df(self):
if self.one_dataset:
try:
self.train_df = pd.read_csv(self.train_data_path)
return (True, self.train_df.keys())
except:
return (False, 'wrong_input_file_format')
else:
try:
self.train_df = pd.read_csv(self.train_data_path)
self.test_df = pd.read_csv(self.test_data_path)
self.generate_test_prediction = True
return (True, self.train_df.keys(), self.test_df.keys())
except:
return (False, 'wrong_input_file_format')
def load_prediction_matrix(self, level):
if level == 0:
self.X = self.feature_processor.fit_transform(self.train_df, True, self.three_way_features, self.two_way_features, self.pair_wise_elimination, self.svd_features, self.Y)
else:
if 'level' in self.history_df.keys():
level_df = self.history_df.loc[history_df['level'] == level]
else:
level_df = self.history_df
if not self.regression:
self.X_p = np.zeros((level_df.shape[0], self.train_df.shape[0], self.n_classes))
else:
self.X_p = np.zeros((level_df.shape[0], self.train_df.shape[0]))
i = 0
for path in level_df['Path'].values:
matrix = np.load(path)
self.X_p[i] = matrix['PREDICTIONS']
i += 1
def build_library(self, level, shared_mem):
past_evals = 0
for learner in self.learners:
opt = HyperoptOptimizer(self.X, self.Y, self.kf, learner, self.metric, path = self.path, maximize = self.metric.maximize, verbose=self.verbose)
bst = opt.optimize(shared_mem, past_evals, self.total_evals, self.n_evals[learner.__name__])
past_evals += self.n_evals[learner.__name__]
df = opt.get_results()
if self.history_df is None:
self.history_df = df
else:
self.history_df = pd.concat([self.history_df, df], axis = 0, ignore_index = True)
if self.metric.maximize:
best_ind = df["Score Mean"].argmax()
else:
best_ind = df["Score Mean"].argmin()
#print df.loc[best_ind]
it = self.best_df.shape[0]
self.best_df.loc[it] = df.loc[best_ind]
#to be implemented
def check_memory(self):
return True
def ensemble(self, level):
if self.ensemble_selection is None:
self.ensemble_selection = ensembleSelection(self.metric, self.kf, self.path)
if self.check_memory():
self.load_prediction_matrix(level)
self.ensemble_selection.es_with_bagging(self.X_p, self.Y)
df = self.ensemble_selection.get_results()
self.history_df = pd.concat([self.history_df, df], axis = 0, ignore_index = True)
if self.stacking is None:
self.stacking = Stacking(self.X, self.Y, self.best_df, self.regression, self.kf, self.path, self.metric)
self.stacking.run()
df = self.stacking.get_results()
self.history_df = pd.concat([self.history_df, df], axis = 0, ignore_index = True)
def final_model(self):
if self.metric.maximize:
best_ind = self.history_df["Score Mean"].argmax()
else:
best_ind = self.history_df["Score Mean"].argmin()
if self.regression:
if self.history_df["Learner"].iloc[best_ind] == 'ensembleSelection':
self.final_model = finalModelESRegressor(self.history_df, best_ind, self.feature_processor)
elif 'Stacking' in self.history_df["Learner"].iloc[best_ind]:
self.final_model = finalModelStackingRegressor(self.history_df, self.best_df, best_ind, self.feature_processor)
else:
self.final_model = finalModelESRegressor(self.history_df, -2, self.feature_processor)
else:
if self.history_df["Learner"].iloc[best_ind] == 'ensembleSelection':
self.final_model = finalModelESClassifier(self.history_df, best_ind, self.feature_processor, self.label_encoder)
elif 'Stacking' in self.history_df["Learner"].iloc[best_ind]:
self.final_model = finalModelStackingClassifier(self.history_df, self.best_df, best_ind, self.feature_processor, self.label_encoder)
else:
self.final_model = finalModelESClassifier(self.history_df, -2, self.feature_processor, self.label_encoder)
self.final_model.fit(self.X, self.Y)
print 'final_model finished'
save(self.path + 'final_model_api' , self.final_model)
def generate_report(self):
if self.regression == True:
classes = None
else:
classes = self.label_encoder.classes_
self.report = ReportGenerator(X_train = self.X, y_train = self.Y,
target_names = classes,
path = self.path,
experiments = self.history_df, num_of_iter= self.total_evals,
maximize = self.metric.maximize, regression = self.regression, elapsed_time= 0,
final_model_time = 0)
self.report.generate()
def run(self, shared_mem = None):
self.load_prediction_matrix(0)
self.build_library(0, shared_mem)
if self.run_ensemble:
self.ensemble(1)
self.history_df.to_csv(self.path + 'hist.csv')
tmp=Shared()
#if self.generate_final_model:
#tmp.write('generating final model', -1)
#shared_mem.put(tmp)
#self.final_model()
#tmp.write('generated final model', -1)
#shared_mem.put(tmp)
tmp.write('generated final report', -1)
shared_mem.put(tmp)
self.generate_report()