def proc2(log): env = make() observation_test = env.reset() #emcv = ElasticNetCV() #columns = ['technical_30', 'technical_20', 'fundamental_11', 'technical_19'] columns = ['technical_30', 'technical_20', 'fundamental_11'] trainCls = TrainData(observation_test.train.copy()) trainCls.graph(2047)
pca = decomp.PCA(n_components = n_comp) pca.fit(data) transformed = pca.transform(data) E = pca.explained_variance_ratio_ #print np.cumsum(E)[::-1][0] #print transformed.shape m = data.shape[0] X = np.hstack( ( np.ones((m, 1)), transformed ) ) #print X.shape return X # -------------------------------------------------------------------------------- env = make() observation = env.reset() train = observation.train.copy() while True: features = observation.features.copy() y_true = env.temp_test_y t_index = TrainFeatCorr(train,features) OLStraining(train,features,t_index,y_true)
def proc1(log): env = make() observation_test = env.reset() emcv = ElasticNetCV() #columns = ['technical_30', 'technical_20', 'fundamental_11', 'technical_19'] columns = ['technical_30', 'technical_20', 'fundamental_11'] train_data = observation_test.train.copy() gmodel_test = glmModel(train_data, columns) y_hat = gmodel_test.BuildModel() model_test = fitModel(emcv, train_data, columns) prediction_test = model_test.predict(observation_test.features.copy()) print "No elasticnet observation :", len(prediction_test) #score_ = r_score(y_true, y_hat) #print score_ return 1 """ train_data = observation_test.train.copy() features_data = observation_test.features.copy() feat_colNames = features_data.columns.values.tolist()[2:] #train_data = observation_test.features.copy kaggleAnalysis = KaggleDataAnalysisClass(train_data,True) kaggleAnalysis.corrCheck(feat_colNames) #emcv = ElasticNetCV(fit_intercept = True) kaggleAnalysis.modelfit(emcv) """ while True: prediction_test = model_test.predict(observation_test.features.copy()) target_test = observation_test.target target_test['y'] = prediction_test """ features_data = observation_test.features.copy() prediction_test = kaggleAnalysis.predict(features_data) target_test = observation_test.target target_test['y'] = prediction_test timestamp_ = observation_test.features["timestamp"][0] log.info("timestamp : %d " % timestamp_) """ timestamp_ = observation_test.features["timestamp"][0] rewards = [] if timestamp_ % 100 == 0: print(timestamp_) y_true = env.temp_test_y score_ = r_score(y_true, prediction_test) rewards.append(score_) log.info("score %.5f" % np.mean(rewards)) observation_test, reward_test, done_test, info_test = env.step( target_test) #log.info("reward_test : %.5f " % reward_test) if done_test: print('Info-test:', info_test['public_score']) break