def get_training_data(training_file, test_file):
'''
Loads training data.
'''
# load training data
df = pd.read_csv(training_file)
# map y values to integers
df['Label'] = df['Label'].map({'b': 0, 's': 1})
# rearrange columns for convenience
cols = df.columns.tolist()
cols = [cols[-1]] + cols[:-1]
df = df[cols]
print 'original features'
print df.columns
df_new = add_features(df)
#df_new = df
cols_new = df_new.columns.tolist()
cols_new = cols_new[:32] + cols_new[33:] + [cols_new[32]
] #make the weight to the last
#the ending comma!!
#only remove phi and tau,lep eta
black_list = [
'PRI_met_phi',
'PRI_lep_phi',
'PRI_tau_phi',
'PRI_jet_leading_phi',
'PRI_jet_subleading_phi',
'PRI_tau_eta',
'PRI_lep_eta',
#'PRI_jet_leading_eta','PRI_jet_subleading_eta',#replace with abs values
#'PRI_lep_px','PRI_lep_py','PRI_lep_pz', 'PRI_lep_px_abs','PRI_lep_py_abs',#these raw values are noisy
#'PRI_tau_px','PRI_tau_py','PRI_tau_pz', 'PRI_tau_pz_abs',
#'PRI_jet_leading_px','PRI_jet_leading_py','PRI_jet_leading_pz', #leading pxyz has separation but abs
#'PRI_jet_subleading_px','PRI_jet_subleading_py','PRI_jet_subleading_pz',
]
#experiment if these phi values makes no sense: TRUE phi itself is really noisy as expected
cols_new = [c for c in cols_new if c not in black_list]
df_new = df_new[cols_new]
print 'newly added features'
print df_new.columns
# convert into numpy array
#train_data = df_new.values
print 'select X features ', cols_new[2:-1]
X_new = df_new[cols_new[2:-1]].values
labels = df_new['Label'].values
weights = df_new['Weight'].values
#print 'exporting to csv with additional feat'
#df_new.to_csv('./additional_feat_training.csv')
#sys.exit()
return X_new, labels, weights
def get_training_data(training_file, test_file):
'''
Loads training data.
'''
# load training data
df = pd.read_csv(training_file)
# map y values to integers
df['Label'] = df['Label'].map({'b':0, 's':1})
# rearrange columns for convenience
cols = df.columns.tolist()
cols = [cols[-1]] + cols[:-1]
df = df[cols]
print 'original features'
print df.columns
df_new = add_features(df)
#df_new = df
cols_new = df_new.columns.tolist()
cols_new = cols_new[:32]+cols_new[33:]+[cols_new[32]] #make the weight to the last
#the ending comma!!
#only remove phi and tau,lep eta
black_list = ['PRI_met_phi', 'PRI_lep_phi', 'PRI_tau_phi', 'PRI_jet_leading_phi','PRI_jet_subleading_phi',
'PRI_tau_eta','PRI_lep_eta',
#'PRI_jet_leading_eta','PRI_jet_subleading_eta',#replace with abs values
#'PRI_lep_px','PRI_lep_py','PRI_lep_pz', 'PRI_lep_px_abs','PRI_lep_py_abs',#these raw values are noisy
#'PRI_tau_px','PRI_tau_py','PRI_tau_pz', 'PRI_tau_pz_abs',
#'PRI_jet_leading_px','PRI_jet_leading_py','PRI_jet_leading_pz', #leading pxyz has separation but abs
#'PRI_jet_subleading_px','PRI_jet_subleading_py','PRI_jet_subleading_pz',
]
#experiment if these phi values makes no sense: TRUE phi itself is really noisy as expected
cols_new = [c for c in cols_new if c not in black_list]
df_new=df_new[cols_new]
print 'newly added features'
print df_new.columns
# convert into numpy array
#train_data = df_new.values
print 'select X features ', cols_new[2:-1]
X_new = df_new[cols_new[2:-1]].values
labels = df_new['Label'].values
weights = df_new['Weight'].values
#print 'exporting to csv with additional feat'
#df_new.to_csv('./additional_feat_training.csv')
#sys.exit()
return X_new, labels, weights
# rearrange columns for convenience
cols = df.columns.tolist()
cols = [cols[-1]] + cols[:-1]
df = df[cols]
df_new = add_features(df)
cols_new = df_new.columns.tolist()
cols_new = cols_new[:32]+cols_new[33:]+[cols_new[32]] #make the weight to the last
df_new=df_new[cols_new]
X_new = df_new[cols_new[2:-1]].values
#idx = dtest[:,0]
'''
df_test = pd.read_csv(dpath+'/test.csv')
#df_test.replace(-999.0,0.)
df_test_data = add_features(df_test)
cols_new = df_test_data.columns.tolist()
black_list = ['PRI_met_phi', 'PRI_lep_phi', 'PRI_tau_phi', 'PRI_jet_leading_phi','PRI_jet_subleading_phi',
'PRI_tau_eta','PRI_lep_eta']
cols_new = [c for c in cols_new if c not in black_list]
df_test_data=df_test_data[cols_new]
data = df_test_data.values[:,1:]
#scaler = StandardScaler().fit(np.vstack((X_new, data))) #normalize with training scale
#scaler = MinMaxScaler(feature_range=(-10,10)).fit(np.vstack((X_new, data)))
#data = scaler.transform(data)
idx = df_test_data.values[:,0]
print ('finish loading from csv ')
xgmat = xgb.DMatrix( data, missing = -999.0 )
bst = xgb.Booster({'nthread':16})
bst.load_model( modelfile )
def get_training_data(training_file, test_file):
'''
Loads training data.
'''
# load training data
df = pd.read_csv(training_file)
#df.replace(-999.0,0.)
# map y values to integers
df['Label'] = df['Label'].map({'b':0, 's':1})
# rearrange columns for convenience
cols = df.columns.tolist()
cols = [cols[-1]] + cols[:-1]
df = df[cols]
#print df.columns
df_new = add_features(df)
cols_new = df_new.columns.tolist()
cols_new = cols_new[:32]+cols_new[33:]+[cols_new[32]] #make the weight to the last
#print len(cols_new)
#black_list = ['PRI_met_phi', 'PRI_lep_phi', 'PRI_tau_phi', 'PRI_jet_leading_phi','PRI_jet_subleading_phi',
# 'PRI_tau_eta','PRI_lep_eta'
# ]
black_list = ['PRI_met_phi', 'PRI_lep_phi', 'PRI_tau_phi', 'PRI_jet_leading_phi','PRI_jet_subleading_phi',
'PRI_tau_eta','PRI_lep_eta',
'PRI_jet_leading_eta','PRI_jet_subleading_eta',#replace with abs values
'PRI_lep_px','PRI_lep_py','PRI_lep_pz', 'PRI_lep_px_abs','PRI_lep_py_abs',#these raw values are noisy
'PRI_tau_px','PRI_tau_py','PRI_tau_pz', 'PRI_tau_pz_abs',
'PRI_jet_leading_px','PRI_jet_leading_py','PRI_jet_leading_pz', #leading pxyz has separation but abs
'PRI_jet_subleading_px','PRI_jet_subleading_py','PRI_jet_subleading_pz',
]
#experiment if these phi values makes no sense: TRUE phi itself is really noisy as expected
cols_new = [c for c in cols_new if c not in black_list]
df_new=df_new[cols_new]
#print df_new.columns
X_new = df_new[cols_new[2:-1]].values
labels = df_new['Label'].values
weights = df_new['Weight'].values
#load test data for better scaling
#df_test = pd.read_csv(test_file)
#df_test.replace(-999.0,0.)
#df_test_data = add_features(df_test)
#X_test = df_test_data.values[:,1:]
#tree-based feature selection
#treeClf = ExtraTreesClassifier()
#print 'feature selection'
#treeClf = AdaBoostClassifier(n_estimators=1000,learning_rate=0.1)
#X_new = treeClf.fit(X, labels).transform(X)
#print treeClf.feature_importances_
#print X_new.shape
#scaler = StandardScaler().fit(X_new)
#standardize the training along with the test scale
#scaler = StandardScaler().fit(np.vstack((X_new, X_test)))
#scaler = MinMaxScaler(feature_range=(-10,10)).fit(np.vstack((X_new, X_test)))
#scaler = Binarizer().fit(np.vstack((X_new, X_test)))
#X_new = scaler.transform(X_new)
return X_new, labels, weights
def get_training_data(training_file, test_file):
'''
Loads training data.
'''
# load training data
df = pd.read_csv(training_file)
#df.replace(-999.0,0.)
# map y values to integers
df['Label'] = df['Label'].map({'b': 0, 's': 1})
# rearrange columns for convenience
cols = df.columns.tolist()
cols = [cols[-1]] + cols[:-1]
df = df[cols]
#print df.columns
df_new = add_features(df)
cols_new = df_new.columns.tolist()
cols_new = cols_new[:32] + cols_new[33:] + [cols_new[32]
] #make the weight to the last
#print len(cols_new)
#black_list = ['PRI_met_phi', 'PRI_lep_phi', 'PRI_tau_phi', 'PRI_jet_leading_phi','PRI_jet_subleading_phi',
# 'PRI_tau_eta','PRI_lep_eta'
# ]
black_list = [
'PRI_met_phi',
'PRI_lep_phi',
'PRI_tau_phi',
'PRI_jet_leading_phi',
'PRI_jet_subleading_phi',
'PRI_tau_eta',
'PRI_lep_eta',
'PRI_jet_leading_eta',
'PRI_jet_subleading_eta', #replace with abs values
'PRI_lep_px',
'PRI_lep_py',
'PRI_lep_pz',
'PRI_lep_px_abs',
'PRI_lep_py_abs', #these raw values are noisy
'PRI_tau_px',
'PRI_tau_py',
'PRI_tau_pz',
'PRI_tau_pz_abs',
'PRI_jet_leading_px',
'PRI_jet_leading_py',
'PRI_jet_leading_pz', #leading pxyz has separation but abs
'PRI_jet_subleading_px',
'PRI_jet_subleading_py',
'PRI_jet_subleading_pz',
]
#experiment if these phi values makes no sense: TRUE phi itself is really noisy as expected
cols_new = [c for c in cols_new if c not in black_list]
df_new = df_new[cols_new]
#print df_new.columns
X_new = df_new[cols_new[2:-1]].values
labels = df_new['Label'].values
weights = df_new['Weight'].values
#load test data for better scaling
#df_test = pd.read_csv(test_file)
#df_test.replace(-999.0,0.)
#df_test_data = add_features(df_test)
#X_test = df_test_data.values[:,1:]
#tree-based feature selection
#treeClf = ExtraTreesClassifier()
#print 'feature selection'
#treeClf = AdaBoostClassifier(n_estimators=1000,learning_rate=0.1)
#X_new = treeClf.fit(X, labels).transform(X)
#print treeClf.feature_importances_
#print X_new.shape
#scaler = StandardScaler().fit(X_new)
#standardize the training along with the test scale
#scaler = StandardScaler().fit(np.vstack((X_new, X_test)))
#scaler = MinMaxScaler(feature_range=(-10,10)).fit(np.vstack((X_new, X_test)))
#scaler = Binarizer().fit(np.vstack((X_new, X_test)))
#X_new = scaler.transform(X_new)
return X_new, labels, weights
from add_features import add_features
from make_msci_regions import add_msci_regions
from calculate_returns2 import add_returns
if True:
start = time.time()
print("Started data processing...")
refresh_data()
print("Adding MSCI regions...")
add_msci_regions()
print("Checking dates and prices...")
check_data_and_prices()
print("Calculating returns...")
add_returns()
print("Calculating volatility, beta, ratios, market cap...")
add_features()
end = time.time()
print("Done! Task took {} seconds.\n\n".format(end - start))
print(
"WARN: RUN data_process_msci FROM ml_advisor TO ENCODE regions AND sectors"
)
del start, end
if True:
fundamentals_2016 = pd.read_hdf(
"../sources/fundamentals_2016_with_feat_msci_regions.hdf5",
"dataset1/x")
fundamentals_2017 = pd.read_hdf(
"../sources/fundamentals_2017_with_feat_msci_regions.hdf5",
"dataset1/x")
