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