<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Import
Step1:  Reading initial data
Step2:  Remove rows with NAN from data
Step3:  Add diff_pt and cos(diff_phi)
Step4:  Add max, sum among PIDs
Step5:  define label = signB * signTrack
if > 0 (same sign) - label 1
if < 0 (different sign) - label 0
Step6:  Apply ghost prob cut
Step7:  Leave not muons, kaons, electrons, protons, pions
Step8:  Tracks sum of charges asymmetry checks
Step9:  Calculating tagging efficiency ($\epsilon_{tag}$)
$$N (\text{passed selection}) = \sum_{\text{passed selection}} sw_i$$
$$N (\text{all events}) = \sum_{\text{all events}} sw_i,$$
where $sw_i$ - sPLot weight (sWeight for signal)
$$\epsilon_{tag} = \frac{N (\text{passed selection})} {N (\text{all events})}$$
$$\Delta\epsilon_{tag} = \frac{\sqrt{\epsilon_{tag}(1-\epsilon_{tag}) \sum_{\text{all events}}sw_i^2}} {N (\text{all events})}$$
All events are not availables (some selections are applyed before), that is why we used 
$$\Delta\epsilon_{tag} = \frac{\sqrt{N (\text{passed selection})}} {N (\text{all events})},$$
which is similar to the previous definition
Step10:  Choose most probable B-events
Step11:  Define B-like events for training
Events with low sWeight still will be used only to test quality.
Step12:  Main idea
Step13:  PID pairs scatters
Step14:  pt
Step15:  count of tracks
Step16:  PIDs histograms
Step17:  Train to distinguish same sign vs opposite sign
Step18:  DT
Step19:  Calibration
Step20:  Implementing best tracking
Step21:  MC trained algorithm testing
<ASSISTANT_TASK:>
Python Code:
import pandas
import numpy
from folding_group import FoldingGroupClassifier
from rep.data import LabeledDataStorage
from rep.report import ClassificationReport
from rep.report.metrics import RocAuc
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve, roc_auc_score
from utils import get_N_B_events, get_events_number, get_events_statistics
Explanation: Import
End of explanation
import root_numpy
data_nan = pandas.DataFrame(root_numpy.root2array('datasets/data/csv/JPsiK/Tracks.root'))
data_nan.head()
event_id_column = 'event_id'
event_id = data_nan.run.apply(str) + '_' + data_nan.event.apply(str)
data_nan['group_column'] = numpy.unique(event_id, return_inverse=True)[1]
data_nan[event_id_column] = event_id
get_events_statistics(data_nan)
get_N_B_events()
Explanation: Reading initial data
End of explanation
data = data_nan.dropna()
len(data_nan), len(data), get_events_statistics(data)
Explanation: Remove rows with NAN from data
End of explanation
from utils import add_diff_pt
# add diff pt
add_diff_pt(data)
# add cos(diff_phi)
data['cos_diff_phi'] = numpy.cos(data.diff_phi.values)
Explanation: Add diff_pt and cos(diff_phi)
End of explanation
from itertools import combinations
PIDs = {'k': data.PIDNNk.values,
        'e': data.PIDNNe.values,
        'mu': data.PIDNNm.values,
       }
for (pid_name1, pid_values1), (pid_name2, pid_values2) in combinations(PIDs.items(), 2):
    data.loc[:, 'max_PID_{}_{}'.format(pid_name1, pid_name2)] = numpy.maximum(pid_values1, pid_values2)
    data.loc[:, 'sum_PID_{}_{}'.format(pid_name1, pid_name2)] = pid_values1 + pid_values2
Explanation: Add max, sum among PIDs
End of explanation
data.loc[:, 'label'] = (data.signB.values * data.signTrack.values > 0) * 1
', '.join(data.columns)
Explanation: define label = signB * signTrack
if > 0 (same sign) - label 1
if < 0 (different sign) - label 0
End of explanation
initial_cut = '(ghostProb < 0.4)'
data = data.query(initial_cut)
get_events_statistics(data)
Explanation: Apply ghost prob cut
End of explanation
threshold_kaon = 0.
threshold_muon = 0.
threshold_electron = 0.
threshold_pion = 0.
threshold_proton = 0.
cut_pid = " ( (PIDNNk > {trk}) | (PIDNNm > {trm}) | (PIDNNe > {tre}) | (PIDNNpi > {trpi}) | (PIDNNp > {trp})) "
cut_pid = cut_pid.format(trk=threshold_kaon, trm=threshold_muon, tre=threshold_electron, trpi=threshold_pion, 
                         trp=threshold_proton)
    
data = data.query(cut_pid)
get_events_statistics(data)
Explanation: Leave not muons, kaons, electrons, protons, pions
End of explanation
from utils import compute_sum_of_charges
means = [compute_sum_of_charges(data[mask], name, bins=bins,
                                event_id_column=event_id_column) for mask, name, bins in \
         zip([data.signB > -100, 
              (data.IPs > 3) & ((abs(data.diff_eta) > 0.6) | (abs(data.diff_phi) > 0.825)), 
              (abs(data.diff_eta) < 0.6) & (abs(data.diff_phi) < 0.825) & (data.IPs < 3)], 
             ['full', 'OS', 'SS'], [21, 21, 21])]
Explanation: Tracks sum of charges asymmetry checks
End of explanation
N_B_passed = float(get_events_number(data))
tagging_efficiency = N_B_passed / get_N_B_events()
tagging_efficiency_delta = sqrt(N_B_passed) / get_N_B_events()
tagging_efficiency, tagging_efficiency_delta
hist(data.diff_pt.values, bins=100)
pass
Explanation: Calculating tagging efficiency ($\epsilon_{tag}$)
$$N (\text{passed selection}) = \sum_{\text{passed selection}} sw_i$$
$$N (\text{all events}) = \sum_{\text{all events}} sw_i,$$
where $sw_i$ - sPLot weight (sWeight for signal)
$$\epsilon_{tag} = \frac{N (\text{passed selection})} {N (\text{all events})}$$
$$\Delta\epsilon_{tag} = \frac{\sqrt{\epsilon_{tag}(1-\epsilon_{tag}) \sum_{\text{all events}}sw_i^2}} {N (\text{all events})}$$
All events are not availables (some selections are applyed before), that is why we used 
$$\Delta\epsilon_{tag} = \frac{\sqrt{N (\text{passed selection})}} {N (\text{all events})},$$
which is similar to the previous definition
End of explanation
_, take_indices = numpy.unique(data[event_id_column], return_index=True)
figure(figsize=[15, 5])
subplot(1, 2, 1)
hist(data.Bmass.values[take_indices], bins=100)
title('B mass hist')
xlabel('mass')
subplot(1, 2, 2)
hist(data.N_sig_sw.values[take_indices], bins=100, normed=True)
title('sWeights hist')
xlabel('signal sWeights')
plt.savefig('img/Bmass_less_PID.png' , format='png')
Explanation: Choose most probable B-events
End of explanation
sweight_threshold = 1.
data_sw_passed = data[data.N_sig_sw > sweight_threshold]
data_sw_not_passed = data[data.N_sig_sw <= sweight_threshold]
get_events_statistics(data_sw_passed)
_, take_indices = numpy.unique(data_sw_passed[event_id_column], return_index=True)
figure(figsize=[15, 5])
subplot(1, 2, 1)
hist(data_sw_passed.Bmass.values[take_indices], bins=100)
title('B mass hist for sWeight > 1 selection')
xlabel('mass')
subplot(1, 2, 2)
hist(data_sw_passed.N_sig_sw.values[take_indices], bins=100, normed=True)
title('sWeights hist for sWeight > 1 selection')
xlabel('signal sWeights')
plt.savefig('img/Bmass_selected_less_PID.png' , format='png')
hist(data_sw_passed.diff_pt.values, bins=100)
pass
Explanation: Define B-like events for training
Events with low sWeight still will be used only to test quality.
End of explanation
features = list(set(data.columns) - {'index', 'run', 'event', 'i', 'signB', 'signTrack', 'N_sig_sw', 'Bmass', 'mult', 
                                     'PIDNNp', 'PIDNNpi', 'label', 'thetaMin', 'Dist_phi', event_id_column, 
                                     'mu_cut', 'e_cut', 'K_cut', 'ID', 'diff_phi', 'group_column'})
features
Explanation: Main idea:
find tracks, which can help reconstruct the sign of B if you know track sign.
label = signB * signTrack
* the highest output means that this is same sign B as track
* the lowest output means that this is opposite sign B than track
Define features
End of explanation
figure(figsize=[15, 16])
bins = 60
step = 3
for i, (feature1, feature2) in enumerate(combinations(['PIDNNk', 'PIDNNm', 'PIDNNe', 'PIDNNp', 'PIDNNpi'], 2)):
    subplot(4, 3, i + 1)
    Z, (x, y) = numpy.histogramdd(data_sw_passed[[feature1, feature2]].values, bins=bins, range=([0, 1], [0, 1]))
    pcolor(numpy.log(Z).T, vmin=0)
    xlabel(feature1)
    ylabel(feature2)
    xticks(numpy.arange(bins, step), x[::step]), yticks(numpy.arange(bins, step), y[::step])
plt.savefig('img/PID_selected_less_PID.png' , format='png')
Explanation: PID pairs scatters
End of explanation
hist(data_sw_passed.diff_pt.values, bins=60, normed=True)
pass
Explanation: pt
End of explanation
figure(figsize=(20, 6))
subplot(1, 2, 1)
_, n_tracks = numpy.unique(data_sw_passed[event_id_column], return_counts=True)
hist(n_tracks, bins=100)    
title('Number of tracks for events with sWeight > 1')
subplot(1, 2, 2)
_, n_tracks_all = numpy.unique(data[event_id_column], return_counts=True)
hist(n_tracks_all, bins=106)    
title('Number of tracks')
plt.savefig('img/tracks_number_less_PID.png' , format='png')
Explanation: count of tracks
End of explanation
figure(figsize=[15, 4])
for i, column in enumerate(['PIDNNm', 'PIDNNe', 'PIDNNk']):
    subplot(1, 3, i + 1)
    hist(data_sw_passed[column].values, bins=60, range=(0, 1), label=column)
    legend()
Explanation: PIDs histograms
End of explanation
from decisiontrain import DecisionTrainClassifier
from rep.estimators import SklearnClassifier
from hep_ml.losses import LogLossFunction
data_sw_passed_lds = LabeledDataStorage(data_sw_passed, data_sw_passed.label.values, data_sw_passed.N_sig_sw.values)
Explanation: Train to distinguish same sign vs opposite sign
End of explanation
tt_base = DecisionTrainClassifier(learning_rate=0.1, n_estimators=3000, depth=6,
                                  max_features=15, n_threads=14, loss=LogLossFunction(regularization=100))
tt_folding = FoldingGroupClassifier(SklearnClassifier(tt_base), n_folds=2, random_state=11, 
                                    train_features=features, group_feature='group_column')
%time tt_folding.fit_lds(data_sw_passed_lds)
pass
import cPickle
with open('models/dt_full_group.pkl', 'w') as f:
    cPickle.dump(tt_folding, f)
# import cPickle
# with open('models/dt_full_group.pkl', 'r') as f:
#     tt_folding = cPickle.load(f)
comparison_report = tt_folding.test_on_lds(data_sw_passed_lds)
comparison_report.compute_metric(RocAuc())
comparison_report.roc()
lc = comparison_report.learning_curve(RocAuc(), steps=1)
lc
comparison_report.feature_importance()
Explanation: DT
End of explanation
from utils import get_result_with_bootstrap_for_given_part
result = get_result_with_bootstrap_for_given_part(tagging_efficiency, tagging_efficiency_delta, tt_folding, 
                                                 [data_sw_passed, data_sw_not_passed], 'tt-log', get_N_B_events(),
                                                 logistic=True, n_calibrations=30)
result
import utils
reload(utils)
from utils import get_result_with_bootstrap_for_given_part
result = get_result_with_bootstrap_for_given_part(tagging_efficiency, tagging_efficiency_delta, tt_folding, 
                                                 [data_sw_passed, data_sw_not_passed], 'tt-log', get_N_B_events(),
                                                 logistic=True, n_calibrations=1)
result
result.to_csv('img/tracks.csv', index=False, header=True)
Explanation: Calibration
End of explanation
from utils import prepare_B_data_for_given_part
Bdata_prepared = prepare_B_data_for_given_part(tt_folding, [data_sw_passed, data_sw_not_passed], 
                                               get_N_B_events(), logistic=True)
Bdata_prepared.to_csv('models/Bdata_tracks.csv', header=True, index=False)
Explanation: Implementing best tracking
End of explanation
from utils import estimate_algorithm
import cPickle
with open('models/dt_MC.pkl', 'r') as f:
    tt_folding_MC = cPickle.load(f)
with open('models/calibrator_tracks_MC.pkl', 'r') as f:
    calibrator_tracks_MC = cPickle.load(f)
with open('models/calibrator_B_MC.pkl', 'r') as f:
    calibrator_B_MC = cPickle.load(f)
p_MC = tt_folding_MC.predict_proba(data)[:, 1]
roc_auc_score(data.label, p_MC, sample_weight=data.N_sig_sw.values.astype(float64))
estimate_algorithm(tt_folding_MC, calibrator_tracks_MC, calibrator_B_MC, data, get_N_B_events())
estimate_algorithm(tt_folding_MC, calibrator_tracks_MC, calibrator_B_MC, data, get_N_B_events(), calib_part_itself=True,
                   calib_itself=True)
Explanation: MC trained algorithm testing
End of explanation
<END_TASK>