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htoaa_predict.py
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htoaa_predict.py
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import pickle
import numpy as np
import pandas as pd
from analib import PhysObj, Event
from info import trainVars, allVars, cutVars, cutDict, weightDict
from data_manager import processData
import collections
import matplotlib.pyplot as plt
## prepare testdata for prediction
fileName = 'GGH_HPT'
data = processData(fileName)
## drop all columns and rows that all nan, then fill nan->0
data = data.dropna(axis = 1, how = 'all')
data = data.dropna(how = 'all')
data = data.fillna(0)
## load model from file
loaded_model = pickle.load(open('XGB_classifier_8Var.pkl', 'rb'))
## make predictions for test data
prediction = loaded_model.predict(data.iloc[:,:-2])
count = collections.Counter(prediction)
## am i also making plots of how many signal and background we found
## for each region of bin?
## what is my bin even???????
## uh this feels wrong.
hist = plt.hist(prediction, 2)
plt.title("prediction of number of signal (1) and background(0) events")
plt.savefig("loadedModel/prediction.png")
plt.clf()
## get column names (without the weight, target)
colNames = list(data.columns)
colNames = colNames[:-2]
## distribution plots
for colName in colNames:
#hist_params = {'density': Fase, 'histtype': 'bar', 'fill': True , 'lw':3, 'alpha' : 0.4}
nbins = 8
dist = plt.hist(data[colName].values, nbins)
# min_valueS, max_valueS = np.percentile(dataSig[colName], [0.0, 99])
# min_valueB, max_valueB = np.percentile(dataBg[colName], [0.0, 99])
# range_local = (min(min_valueS,min_valueB), max(max_valueS,max_valueB))
# valuesS, binsS, _ = plt.hist(
# dataSig[colName].values,
# range = range_local,
# bins = nbins, edgecolor='b', color='b',
# label = "Signal", **hist_params
# )
# to_ymax = max(valuesS)
# to_ymin = min(valuesS)
# valuesB, binsB, _ = plt.hist(
# dataBg[colName].values,
# range = range_local,
# bins = nbins, edgecolor='g', color='g',
# label = "Background", **hist_params
# )
# to_ymax2 = max(valuesB)
# to_ymax = max([to_ymax2, to_ymax])
# to_ymin2 = min(valuesB)
# to_ymin = max([to_ymin2, to_ymin])
# plt.ylim(ymin=to_ymin*0.1, ymax=to_ymax*1.2)
# plt.legend(loc='best')
plt.xlabel(colName)
plt.savefig("loadedModel/distributions_%s.png" % colName)
plt.clf()