{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"import pickle\n",
"import math\n",
"\n",
"trunc_normal= tf.truncated_normal_initializer(stddev=1)\n",
"normal = tf.random_normal_initializer(stddev=1)\n",
"\n",
"from architectures.data_processing import *\n",
"from architectures.utils.toolbox import *\n",
"from architectures.DNN import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"l_index=1\n",
"mag_index=1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"with open('/disk/lhcb_data/davide/Rphipi/NN_for_selection/'+l_flv[l_index]+l_flv[l_index]+'/'+'data_for_NN_'+l_flv[l_index]+l_flv[l_index]+'_Mag'+mag_status[mag_index]+'.pickle', 'rb') as f:\n",
" data_dict=pickle.load(f, encoding='latin1')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"\n",
"data_dict[\"Ds_ENDVERTEX_CHI2\"]=data_dict[\"Ds_ENDVERTEX_CHI2\"]/data_dict[\"Ds_ENDVERTEX_NDOF\"]\n",
"data_dict[\"Ds_OWNPV_CHI2\"]=data_dict[\"Ds_OWNPV_CHI2\"]/data_dict[\"Ds_OWNPV_NDOF\"]\n",
"data_dict[\"Ds_IPCHI2_OWNPV\"]=data_dict[\"Ds_IPCHI2_OWNPV\"]/data_dict[\"Ds_ENDVERTEX_NDOF\"]\n",
"\n",
"del data_dict[\"Ds_ENDVERTEX_NDOF\"]\n",
"del data_dict[\"Ds_OWNPV_NDOF\"]\n",
"\n",
"data_dict[\"phi_ENDVERTEX_CHI2\"]=data_dict[\"phi_ENDVERTEX_CHI2\"]/data_dict[\"phi_ENDVERTEX_NDOF\"]\n",
"data_dict[\"phi_OWNPV_CHI2\"]=data_dict[\"phi_OWNPV_CHI2\"]/data_dict[\"phi_OWNPV_NDOF\"]\n",
"data_dict[\"phi_IPCHI2_OWNPV\"]=data_dict[\"phi_IPCHI2_OWNPV\"]/data_dict[\"phi_ENDVERTEX_NDOF\"]\n",
"\n",
"del data_dict[\"phi_ENDVERTEX_NDOF\"]\n",
"del data_dict[\"phi_OWNPV_NDOF\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"branches_needed = [\n",
" \"Ds_ENDVERTEX_CHI2\",\n",
" #\"Ds_ENDVERTEX_NDOF\",\n",
" \"Ds_OWNPV_CHI2\",\n",
" #\"Ds_OWNPV_NDOF\",\n",
" \"Ds_IPCHI2_OWNPV\",\n",
" \"Ds_IP_OWNPV\",\n",
" \"Ds_DIRA_OWNPV\",\n",
" #l_flv[l_index]+\"_plus_MC15TuneV1_ProbNN\"+l_flv[l_index],\n",
" #\"Ds_Hlt1TrackMVADecision_TOS\",\n",
" #\"Ds_Hlt2RareCharmD2Pi\"+l_flv[l_index].capitalize()+l_flv[l_index].capitalize()+\"OSDecision_TOS\",\n",
" #\"Ds_Hlt2Phys_TOS\",\n",
" \"phi_ENDVERTEX_CHI2\",\n",
" #\"phi_ENDVERTEX_NDOF\",\n",
" \"phi_OWNPV_CHI2\",\n",
" #\"phi_OWNPV_NDOF\",\n",
" \"phi_IPCHI2_OWNPV\",\n",
" \"phi_IP_OWNPV\",\n",
" \"phi_DIRA_OWNPV\",\n",
" #\"Ds_ConsD_M\",\n",
" ] "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#Number of input features\n",
"m=data_dict[\"Ds_ConsD_M\"].shape[0]\n",
"dim=len(branches_needed)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"data = extract_array(data_dict, branches_needed, dim, m)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(106404, 10)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"task='TEST'\n",
"\n",
"PATH=l_flv[l_index]+'_Mag'+mag_status[mag_index]+'_test_4'"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"if task == 'TEST' and os.path.exists(PATH+'/hyper_parameters.pkl'):\n",
" with open(PATH+'/hyper_parameters.pkl', 'rb') as f: \n",
" hyper_dict = pickle.load(f)\n",
" #for key, item in hyper_dict.items():\n",
" # print(key+':'+str(item))\n",
" \n",
" #m=hyper_dict[\"m\"]\n",
" test_size=hyper_dict[\"test_size\"]\n",
" val_size=hyper_dict[\"val_size\"]\n",
" LEARNING_RATE=hyper_dict[\"LEARNING_RATE\"]\n",
" BETA1=hyper_dict[\"BETA1\"]\n",
" BATCH_SIZE=hyper_dict[\"BATCH_SIZE\"]\n",
" EPOCHS=hyper_dict[\"EPOCHS\"]\n",
" VAL_PERIOD=hyper_dict[\"VAL_PERIOD\"]\n",
" SEED=hyper_dict[\"SEED\"]\n",
" sizes=hyper_dict[\"sizes\"]\n",
" LAMBD=hyper_dict[\"LAMBD\"]\n",
" PATH=hyper_dict[\"PATH\"]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def bkg(data):\n",
" \n",
" batch_size_output=5000\n",
" n_batches_output = m//batch_size_output\n",
"\n",
" tf.reset_default_graph()\n",
" nn = DNN(dim, sizes,\n",
" lr=LEARNING_RATE, beta1=BETA1, lambd=LAMBD,\n",
" batch_size=BATCH_SIZE, epochs=EPOCHS,\n",
" save_sample=VAL_PERIOD, path=PATH, seed=SEED)\n",
" \n",
" vars_to_train= tf.trainable_variables()\n",
" vars_all = tf.global_variables()\n",
" vars_to_init = list(set(vars_all)-set(vars_to_train))\n",
" init_op = tf.variables_initializer(vars_to_init)\n",
" \n",
" # Add ops to save and restore all the variables.\n",
" saver = tf.train.Saver()\n",
" gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)\n",
" \n",
" with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:\n",
" \n",
" sess.run(init_op)\n",
" print('\\n Selecting signal events with model...')\n",
" saver.restore(sess,PATH+'/CNN_model.ckpt')\n",
" print('Model restored.')\n",
" \n",
" nn.set_session(sess)\n",
" output_dict={}\n",
" \n",
" for i in range(n_batches_output):\n",
" small_dataset = data[i:i+batch_size_output]\n",
" output_dict[i] = nn.predict(small_dataset)\n",
" output=np.concatenate([output_dict[i] for i in range(len(output_dict))])\n",
" return output\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'sizes' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-e6881bf43157>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No checkpoint'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbkg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-11-b7f16e1c1d0b>\u001b[0m in \u001b[0;36mbkg\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_default_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m nn = DNN(dim, sizes,\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mLEARNING_RATE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mBETA1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlambd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mLAMBD\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mEPOCHS\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'sizes' is not defined"
]
}
],
"source": [
"if __name__=='__main__':\n",
" if not os.path.exists(PATH+'/checkpoint'):\n",
" print('No checkpoint')\n",
" else:\n",
" output=bkg(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"output"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.argmax(output, axis=1).astype(np.bool).sum()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a = data_dict[\"Ds_ConsD_M\"][0:output.shape[0]][np.argmax(output, axis=1).astype(np.bool)]\n",
"b = [data_dict[\"Ds_ConsD_M\"][0:output.shape[0]][i] for i in range(output.shape[0])]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"NN_selected=np.array([a[i][0] for i in range(len(a))])\n",
"full = np.array([b[i][0] for i in range(len(b))])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"full=np.delete(full,np.where(full<0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.hist(full,alpha=0.4,bins=100, range=(0,3000));\n",
"plt.hist(NN_selected,alpha=0.4,bins=100, range=(0,3000));\n",
"fig=plt.gcf();\n",
"fig.set_size_inches(16,10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.random.randint(14)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Davide Lancierini