def beatthebenchmark():
#Columns to be picked from training file
pickTrain = ['BSAN','BSAS','BSAV','CTI','ELEV','EVI','LSTD','LSTN','REF1','REF2','REF3','REF7','RELI','TMAP','TMFI','Depth','Ca','P','pH','SOC','Sand']
data = np.genfromtxt(trainloc, names=True, delimiter=',', usecols=(pickTrain))
#Column to be picked from test file
pickTest = ['PIDN', 'BSAN','BSAS','BSAV','CTI','ELEV','EVI','LSTD','LSTN','REF1','REF2','REF3','REF7','RELI','TMAP','TMFI']
test = np.genfromtxt(testloc, names=True, delimiter=',', usecols=(pickTest))
ids = np.genfromtxt(testloc, dtype=str, skip_header=1, delimiter=',', usecols=0)
#Features to train model on
featuresList = ['BSAN','BSAS','BSAV','CTI','ELEV','EVI','LSTD','LSTN','REF1','REF2','REF3','REF7','RELI','TMAP','TMFI']
#Keep a copy of train file for later use
data1 = np.copy(data)
#Dependent/Target variables
targets = ['Ca','P','pH','SOC','Sand']
#Prepare empty result
df = pd.DataFrame({"PIDN": ids, "Ca": test['PIDN'], "P": test['PIDN'], "pH": test['PIDN'], "SOC": test['PIDN'], "Sand": test['PIDN']})
for target in targets:
#Prepare data for training
data, testa, features, fillVal = util.prepDataTrain(data1, target, featuresList, False, 10, False, True, 'mean', False, 'set')
print 'Data preped'
#Use/tune your predictor
clf = ensemble.GradientBoostingRegressor(n_estimators=20)
clf.fit(data[features].tolist(), data[target])
#Prepare test data
test = util.prepDataTest(test, featuresList, True, fillVal, False, 'set')
#Get predictions
pred = clf.predict(test[features].tolist())
#Store results
df[target] = pred
df.to_csv(predloc, index=False, cols=["PIDN","Ca","P","pH","SOC","Sand"])
def classification(data, featuresList):
data["target"][:, (data["target"] > 0)] = 1
data["target"][:, (data["target"] == 0)] = 0
data, testa, features, fillVal = util.prepDataTrain(
data, "target", featuresList, True, 50, True, True, "median", False, "set"
)
print "Data preped"
clf = bayes.GaussianNB()
# clf = tree.DecisionTreeClassifier()
clf.fit(data[features].tolist(), data["target"])
pred = clf.predict_proba(testa[features].tolist())[:, 1]
pred[pred > 0.005] = 1
pred[pred <= 0.005] = 0
res = testa["target"] - pred
print res, pred, testa["target"], len(np.where(res[res < -0.5])[0]), len(np.where(res[res > 0.5])[0]), len(
np.where(testa["target"][testa["target"] > 0.5])[0]
), testa.shape, data.shape
def classification(data, featuresList):
data['target'][:, (data['target'] > 0)] = 1
data['target'][:, (data['target'] == 0)] = 0
data, testa, features, fillVal = util.prepDataTrain(
data, 'target', featuresList, True, 50, True, True, 'median', False,
'set')
print 'Data preped'
clf = bayes.GaussianNB()
#clf = tree.DecisionTreeClassifier()
clf.fit(data[features].tolist(), data['target'])
pred = clf.predict_proba(testa[features].tolist())[:, 1]
pred[pred > .005] = 1
pred[pred <= .005] = 0
res = testa['target'] - pred
print res, pred, testa['target'], len(np.where(res[res < -.5])[0]), len(
np.where(res[res > .5])[0]), len(
np.where(testa['target'][testa['target'] > .5])
[0]), testa.shape, data.shape
clf = 0
print target
'''
for i in range(len(data)):
if data[target][i] > (data[target].mean() + 2*data[target].std()) or data[target][i] < (data[target].mean() - 2*data[target].std()):
delList = np.append(delList, i)
print (data[target].mean() - 1*data[target].std()), data[target].std()
'''
#clf = linear.BayesianRidge(verbose=True, alpha_1=2, alpha_2=2, lambda_1=.01, lambda_2=.01, fit_intercept=True, compute_score=True)
#clf = linear.BayesianRidge(verbose=True)
#clf = tree.DecisionTreeRegressor(max_depth=2)
clf = svm.SVR(C=10000.0, kernel='rbf', degree=1)
data = np.delete(data, delList, 0)
data, testa, features, fillVal = util.prepDataTrain(data, target, featuresList, False, 20, False, True, 'mean', False, 'set')
data = recfunctions.rec_drop_fields(data, delFeatures)
#features = ['CTI','Depth', 'RELI', 'LSTN']
#an.plotData(np.sqrt(1+data['P']), data['ELEV']*(-1*data['TMAP']))
#data, clust, enc, newCol = clusterData(data, clusterFields, True)
#testa, clust, enc, newCol = clusterData(testa, pickTest, True, enc, clust, False)
#features = np.concatenate((features, newCol))
#Use/tune your predictor
#clf.fit(data[features].tolist(), data[target])
#import pydot
#dot_data = StringIO.StringIO()
#tree.export_graphviz(clf, out_file=dot_data)
#graph = pydot.graph_from_dot_data(dot_data.getvalue())
#graph.write_pdf("./ds.pdf")
import numpy as np
import utilities as util
import sklearn.linear_model as linear
import sklearn.ensemble as ensemble
from sklearn import cross_validation
import pandas as pd
data = np.genfromtxt('../training.csv', names=True, delimiter=',')
test = np.genfromtxt('../test.csv', names=True, delimiter=',')
featuresList = ['DER_mass_MMC','DER_mass_transverse_met_lep','DER_mass_vis','DER_pt_h','DER_deltaeta_jet_jet','DER_mass_jet_jet','DER_prodeta_jet_jet','DER_deltar_tau_lep','DER_pt_tot','DER_sum_pt','DER_pt_ratio_lep_tau','DER_met_phi_centrality','DER_lep_eta_centrality','PRI_tau_pt','PRI_tau_eta','PRI_tau_phi','PRI_lep_pt','PRI_lep_eta','PRI_lep_phi','PRI_met','PRI_met_phi','PRI_met_sumet','PRI_jet_num','PRI_jet_leading_pt','PRI_jet_leading_eta','PRI_jet_leading_phi','PRI_jet_subleading_pt','PRI_jet_subleading_eta','PRI_jet_subleading_phi','PRI_jet_all_pt']
data, testa, features, fillVal = util.prepDataTrain(data, 'Label', featuresList, False, 10, False, True, 'mean', False, 'set')
print 'Data preped'
clf = ensemble.GradientBoostingClassifier(n_estimators=50)
#clf = ensemble.BaggingClassifier()
clf.fit(data[features].tolist(), data['Label'])
#scores = cross_validation.cross_val_score(clf, data[features].tolist(), data['Label'], cv=5, scoring='f1')
#print scores
#print clf.score(test[features].tolist(), test['Label'])
print 'fitted'
pcut = .50
ids = test['EventId'].astype(int)
X_test = util.prepDataTest(test, featuresList, True, fillVal, False, 'set')
#data = pd.read_csv("../test.csv")
#X_test = data.values[:, 1:]
data1 = np.copy(data)
featuresList = [
'weatherVar185', 'weatherVar21', 'weatherVar189', 'weatherVar161',
'weatherVar103', 'weatherVar95', 'weatherVar194', 'weatherVar216',
'weatherVar186', 'weatherVar110', 'weatherVar137', 'weatherVar23',
'weatherVar49', 'weatherVar232', 'weatherVar68', 'weatherVar22',
'weatherVar151', 'weatherVar16', 'geodemVar14', 'geodemVar29', 'var8',
'var4', 'var10', 'var11', 'var12', 'var13', 'var15', 'var17'
]
#Cross validation testscores
for i in ([0, 1]):
data = np.copy(data1)
data, testa, features, fillVal = util.prepDataTrain(
data, 'target', featuresList, True, 50, False, True, 'median', False,
'set', i)
data['target'] = np.log(math.e + data['target'])
data['target'][
data['target'] > 3] = 3 #np.log(data['target'][data['target'] > 10])
print 'Data preped'
clf = ensemble.GradientBoostingRegressor(n_estimators=45,
max_depth=5,
min_samples_leaf=20,
min_samples_split=30,
verbose=True,
loss='ls')
clf.fit(data[features].tolist(), data['target'])
print 'fitted'
#Features to train model on
featuresList = ['BSAN','BSAS','BSAV','CTI','ELEV','EVI','LSTD','LSTN','REF1','REF2','REF3','REF7','RELI','TMAP','TMFI']
#Keep a copy of train file for later use
data1 = np.copy(data)
#Dependent/Target variables
targets = ['Ca','P','pH','SOC','Sand']
#Prepare empty result
df = pd.DataFrame({"PIDN": ids, "Ca": test['PIDN'], "P": test['PIDN'], "pH": test['PIDN'], "SOC": test['PIDN'], "Sand": test['PIDN']})
for target in targets:
#Prepare data for training
data, testa, features, fillVal = util.prepDataTrain(data1, target, featuresList, False, 10, False, True, 'mean', False, 'set')
print 'Data preped'
#Use/tune your predictor
clf = ensemble.GradientBoostingRegressor(n_estimators=20)
clf.fit(data[features].tolist(), data[target])
#Prepare test data
test = util.prepDataTest(test, featuresList, True, fillVal, False, 'set')
#Get predictions
pred = clf.predict(test[features].tolist())
#Store results
df[target] = pred
"geodemVar29",
"var8",
"var4",
"var10",
"var11",
"var12",
"var13",
"var15",
"var17",
]
# Cross validation testscores
for i in [0, 1]:
data = np.copy(data1)
data, testa, features, fillVal = util.prepDataTrain(
data, "target", featuresList, True, 50, False, True, "median", False, "set", i
)
data["target"] = np.log(math.e + data["target"])
data["target"][data["target"] > 3] = 3 # np.log(data['target'][data['target'] > 10])
print "Data preped"
clf = ensemble.GradientBoostingRegressor(
n_estimators=45, max_depth=5, min_samples_leaf=20, min_samples_split=30, verbose=True, loss="ls"
)
clf.fit(data[features].tolist(), data["target"])
print "fitted"
pred = np.power(clf.predict(testa[features].tolist()), math.e)
print normalized_weighted_gini(testa["target"], pred, testa["var11"])
# for i in range(len(clf.feature_importances_)):
def beatthebenchmark():
#Columns to be picked from training file
pickTrain = [
'BSAN', 'BSAS', 'BSAV', 'CTI', 'ELEV', 'EVI', 'LSTD', 'LSTN', 'REF1',
'REF2', 'REF3', 'REF7', 'RELI', 'TMAP', 'TMFI', 'Depth', 'Ca', 'P',
'pH', 'SOC', 'Sand'
]
data = np.genfromtxt(trainloc,
names=True,
delimiter=',',
usecols=(pickTrain))
#Column to be picked from test file
pickTest = [
'PIDN', 'BSAN', 'BSAS', 'BSAV', 'CTI', 'ELEV', 'EVI', 'LSTD', 'LSTN',
'REF1', 'REF2', 'REF3', 'REF7', 'RELI', 'TMAP', 'TMFI'
]
test = np.genfromtxt(testloc,
names=True,
delimiter=',',
usecols=(pickTest))
ids = np.genfromtxt(testloc,
dtype=str,
skip_header=1,
delimiter=',',
usecols=0)
#Features to train model on
featuresList = [
'BSAN', 'BSAS', 'BSAV', 'CTI', 'ELEV', 'EVI', 'LSTD', 'LSTN', 'REF1',
'REF2', 'REF3', 'REF7', 'RELI', 'TMAP', 'TMFI'
]
#Keep a copy of train file for later use
data1 = np.copy(data)
#Dependent/Target variables
targets = ['Ca', 'P', 'pH', 'SOC', 'Sand']
#Prepare empty result
df = pd.DataFrame({
"PIDN": ids,
"Ca": test['PIDN'],
"P": test['PIDN'],
"pH": test['PIDN'],
"SOC": test['PIDN'],
"Sand": test['PIDN']
})
for target in targets:
#Prepare data for training
data, testa, features, fillVal = util.prepDataTrain(
data1, target, featuresList, False, 10, False, True, 'mean', False,
'set')
print 'Data preped'
#Use/tune your predictor
clf = ensemble.GradientBoostingRegressor(n_estimators=20)
clf.fit(data[features].tolist(), data[target])
#Prepare test data
test = util.prepDataTest(test, featuresList, True, fillVal, False, 'set')
#Get predictions
pred = clf.predict(test[features].tolist())
#Store results
df[target] = pred
df.to_csv(predloc,
index=False,
cols=["PIDN", "Ca", "P", "pH", "SOC", "Sand"])