From f3cdc57a80715083a7e141a242376242e7bd1bd2 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Thu, 22 Feb 2018 11:53:17 +0100 Subject: [PATCH] Add support for convolutions #1 Added a jupyter notebook --- doc/keras_mnist.ipynb | 3367 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 3367 insertions(+) create mode 100644 doc/keras_mnist.ipynb diff --git a/doc/keras_mnist.ipynb b/doc/keras_mnist.ipynb new file mode 100644 index 0000000..39ce8af --- /dev/null +++ b/doc/keras_mnist.ipynb @@ -0,0 +1,3367 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/antoine/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n", + "/usr/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n", + " return f(*args, **kwds)\n" + ] + } + ], + "source": [ + "import os, numpy as np\n", + "import pandas as pd\n", + "\n", + "\n", + "\n", + "from __future__ import print_function\n", + "import keras\n", + "from keras.datasets import mnist\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Dropout, Flatten\n", + "from keras.layers import Conv2D, MaxPooling2D\n", + "from keras import backend as K" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Build a Keras Model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_train shape: (6000, 28, 28, 1)\n", + "6000 train samples\n", + "1000 test samples\n" + ] + } + ], + "source": [ + "# shameless copy of keras example : https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n", + "\n", + "sample = True\n", + "\n", + "batch_size = 128\n", + "num_classes = 10\n", + "epochs = 12\n", + "\n", + "# input image dimensions\n", + "img_rows, img_cols = 28, 28\n", + "\n", + "# the data, shuffled and split between train and test sets\n", + "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", + "\n", + "if K.image_data_format() == 'channels_first':\n", + " x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", + " x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", + " input_shape = (1, img_rows, img_cols)\n", + "else:\n", + " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", + " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", + " input_shape = (img_rows, img_cols, 1)\n", + "\n", + "x_train = x_train.astype('float32')\n", + "x_test = x_test.astype('float32')\n", + "x_train /= 255\n", + "x_test /= 255\n", + "\n", + "\n", + "if(sample):\n", + " indices = np.random.choice(x_train.shape[0], x_train.shape[0] // 10, replace=False)\n", + " x_train = x_train[indices, : , :, :]\n", + " y_train = y_train[indices]\n", + " indices = np.random.choice(x_test.shape[0], x_test.shape[0] // 10, replace=False)\n", + " x_test = x_test[indices, : , :, :]\n", + " y_test = y_test[indices]\n", + "\n", + "\n", + "print('x_train shape:', x_train.shape)\n", + "print(x_train.shape[0], 'train samples')\n", + "print(x_test.shape[0], 'test samples')\n", + "\n", + "\n", + "\n", + "# convert class vectors to binary class matrices\n", + "y_train = keras.utils.to_categorical(y_train, num_classes)\n", + "y_test = keras.utils.to_categorical(y_test, num_classes)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def create_model():\n", + " model = Sequential()\n", + " model.add(Conv2D(8, kernel_size=(3, 3),\n", + " activation='relu',\n", + " input_shape=input_shape))\n", + " # model.add(Conv2D(4, (3, 3), activation='relu'))\n", + " model.add(MaxPooling2D(pool_size=(2, 2)))\n", + " model.add(Dropout(0.25))\n", + " model.add(Flatten())\n", + " model.add(Dense(128, activation='relu'))\n", + " model.add(Dropout(0.5))\n", + " model.add(Dense(num_classes, activation='softmax'))\n", + "\n", + " model.compile(loss=keras.losses.categorical_crossentropy,\n", + " optimizer=keras.optimizers.Adadelta(),\n", + " metrics=['accuracy'])\n", + "\n", + " return model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train on 6000 samples, validate on 1000 samples\n", + "Epoch 1/12\n", + "6000/6000 [==============================] - 4s 591us/step - loss: 1.7582 - acc: 0.4460 - val_loss: 0.8198 - val_acc: 0.8450\n", + "Epoch 2/12\n", + "6000/6000 [==============================] - 3s 562us/step - loss: 0.8134 - acc: 0.7495 - val_loss: 0.4193 - val_acc: 0.9050\n", + "Epoch 3/12\n", + "6000/6000 [==============================] - 3s 573us/step - loss: 0.5875 - acc: 0.8193 - val_loss: 0.3247 - val_acc: 0.9220\n", + "Epoch 4/12\n", + "6000/6000 [==============================] - 3s 577us/step - loss: 0.4855 - acc: 0.8533 - val_loss: 0.2750 - val_acc: 0.9290\n", + "Epoch 5/12\n", + "6000/6000 [==============================] - 3s 569us/step - loss: 0.4353 - acc: 0.8687 - val_loss: 0.2453 - val_acc: 0.9370\n", + "Epoch 6/12\n", + "6000/6000 [==============================] - 3s 566us/step - loss: 0.3885 - acc: 0.8817 - val_loss: 0.2308 - val_acc: 0.9400\n", + "Epoch 7/12\n", + "6000/6000 [==============================] - 3s 569us/step - loss: 0.3557 - acc: 0.8908 - val_loss: 0.2074 - val_acc: 0.9430\n", + "Epoch 8/12\n", + "6000/6000 [==============================] - 3s 565us/step - loss: 0.3446 - acc: 0.8960 - val_loss: 0.1986 - val_acc: 0.9490\n", + "Epoch 9/12\n", + "6000/6000 [==============================] - 3s 573us/step - loss: 0.3156 - acc: 0.9030 - val_loss: 0.1879 - val_acc: 0.9460\n", + "Epoch 10/12\n", + "6000/6000 [==============================] - 3s 554us/step - loss: 0.3012 - acc: 0.9070 - val_loss: 0.1818 - val_acc: 0.9530\n", + "Epoch 11/12\n", + "6000/6000 [==============================] - 3s 571us/step - loss: 0.2908 - acc: 0.9087 - val_loss: 0.1721 - val_acc: 0.9500\n", + "Epoch 12/12\n", + "6000/6000 [==============================] - 3s 573us/step - loss: 0.2768 - acc: 0.9207 - val_loss: 0.1661 - val_acc: 0.9570\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "from keras.wrappers.scikit_learn import KerasClassifier\n", + "\n", + "clf = KerasClassifier(build_fn=create_model, epochs=epochs, batch_size=batch_size, verbose=1)\n", + "\n", + "clf.fit(x_train, y_train ,\n", + " batch_size=batch_size,\n", + " epochs=12,\n", + " verbose=1,\n", + " validation_data=(x_test, y_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1000, 28, 28, 1)\n", + "1/1 [==============================] - 0s 2ms/step\n", + "[2]\n" + ] + } + ], + "source": [ + "print(x_test.shape)\n", + "preds = clf.predict(x_test[0,:].reshape(1, 28 , 28, 1))\n", + "print(preds)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate SQL Code from the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import json, requests, base64, dill as pickle, sys\n", + "\n", + "\n", + "\n", + "sys.setrecursionlimit(200000)\n", + "pickle.settings['recurse'] = False\n", + "\n", + "# no luck for the web service... pickling feature of tensorflow and/or keras objects seems not to be a priority.\n", + "# there is a lot of github issues in the two projects when I search for pickle keyword!!!.\n", + "\n", + "def test_ws_sql_gen(pickle_data):\n", + " WS_URL=\"http://localhost:1888/model\"\n", + " b64_data = base64.b64encode(pickle_data).decode('utf-8')\n", + " data={\"Name\":\"model1\", \"PickleData\":b64_data , \"SQLDialect\":\"postgresql\"}\n", + " r = requests.post(WS_URL, json=data)\n", + " print(r.__dict__)\n", + " content = r.json()\n", + " # print(content)\n", + " lSQL = content[\"model\"][\"SQLGenrationResult\"][0][\"SQL\"]\n", + " return lSQL;\n", + "\n", + "\n", + "\n", + "def test_sql_gen(keras_regressor , metadata):\n", + " import sklearn2sql.PyCodeGenerator as codegen\n", + " cg1 = codegen.cAbstractCodeGenerator();\n", + " lSQL = cg1.generateCodeWithMetadata(clf, metadata, dsn = None, dialect = \"postgresql\");\n", + " return lSQL[0]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "# commented .. see above\n", + "# pickle_data = pickle.dumps(clf)\n", + "# lSQL = test_ws_sql_gen(pickle_data)\n", + "# print(lSQL[0:2000])" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cGenerationWrapperFactory::createWrapper() \n", + "cClassifierMixin_CodeGenWrapper::setObject \n", + "BACKEND_DIALECT postgresql\n", + "CREATING_DATABASE_BACKEND_DSN_DIALECT 1.2.2 None postgresql\n", + "KERAS_GENERATE_EXPRESSION_START \n", + "{'mKerasData': , 'mFeatureNames': ['X_1', 'X_2', 'X_3', 'X_4', 'X_5', 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...\n", + " [ 468 469 470 ... 478 479 480]\n", + " [ 481 482 483 ... 491 492 493]\n", + " [ 494 495 496 ... 504 505 506]]\n", + "\n", + " ...\n", + "\n", + " [[ 845 846 847 ... 855 856 857]\n", + " [ 858 859 860 ... 868 869 870]\n", + " [ 871 872 873 ... 881 882 883]\n", + " ...\n", + " [ 975 976 977 ... 985 986 987]\n", + " [ 988 989 990 ... 998 999 1000]\n", + " [1001 1002 1003 ... 1011 1012 1013]]\n", + "\n", + " [[1014 1015 1016 ... 1024 1025 1026]\n", + " [1027 1028 1029 ... 1037 1038 1039]\n", + " [1040 1041 1042 ... 1050 1051 1052]\n", + " ...\n", + " [1144 1145 1146 ... 1154 1155 1156]\n", + " [1157 1158 1159 ... 1167 1168 1169]\n", + " [1170 1171 1172 ... 1180 1181 1182]]\n", + "\n", + " [[1183 1184 1185 ... 1193 1194 1195]\n", + " [1196 1197 1198 ... 1206 1207 1208]\n", + " [1209 1210 1211 ... 1219 1220 1221]\n", + " ...\n", + " [1313 1314 1315 ... 1323 1324 1325]\n", + " [1326 1327 1328 ... 1336 1337 1338]\n", + " [1339 1340 1341 ... 1349 1350 1351]]]\n", + "FLATTEN_INDICES2 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"FLATTEN_JOIN_COL 12 12 0 168 flatten_join.output_0_12_12\n", + "FLATTEN_JOIN_COL 12 12 1 337 flatten_join.output_1_12_12\n", + "FLATTEN_JOIN_COL 12 12 2 506 flatten_join.output_2_12_12\n", + "FLATTEN_JOIN_COL 12 12 3 675 flatten_join.output_3_12_12\n", + "FLATTEN_JOIN_COL 12 12 4 844 flatten_join.output_4_12_12\n", + "FLATTEN_JOIN_COL 12 12 5 1013 flatten_join.output_5_12_12\n", + "FLATTEN_JOIN_COL 12 12 6 1182 flatten_join.output_6_12_12\n", + "FLATTEN_JOIN_COL 12 12 7 1351 flatten_join.output_7_12_12\n", + "ABSTRACT_GENERATE_CTE layer_flatten_2 layer_flatten_2 1353\n", + "GENERATING_LAYER 4 dense_3 {'input_spec': , 'supports_masking': True, '_trainable_weights': [, ], '_non_trainable_weights': [], '_losses': [], '_updates': [], '_per_input_losses': {}, '_per_input_updates': {}, '_built': True, 'inbound_nodes': [], 'outbound_nodes': [], 'name': 'dense_3', 'trainable': True, '_initial_weights': None, 'units': 128, 'activation': , 'use_bias': True, 'kernel_initializer': , 'bias_initializer': , 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'kernel': , 'bias': }\n", + "LAYER_CHECK_COLUMNS ('dense_3', , (None, 1352), 1353, 1353)\n", + "KERAS_GENERATE_LAYER_START \n", + "ABSTRACT_GENERATE_CTE layer_dense_3 layer_dense_3 129\n", + "ABSTRACT_GENERATE_CTE activation_relu activation_relu 129\n", + "GENERATING_LAYER 5 dropout_4 {'input_spec': None, 'supports_masking': True, '_trainable_weights': [], '_non_trainable_weights': [], '_losses': [], '_updates': [], '_per_input_losses': {}, '_per_input_updates': {}, '_built': True, 'inbound_nodes': [], 'outbound_nodes': [], 'name': 'dropout_4', 'trainable': True, '_initial_weights': None, 'rate': 0.5, 'noise_shape': None, 'seed': None}\n", + "LAYER_CHECK_COLUMNS ('dropout_4', , (None, 128), 129, 129)\n", + "KERAS_GENERATE_LAYER_START \n", + "GENERATING_LAYER 6 dense_4 {'input_spec': , 'supports_masking': True, '_trainable_weights': [, ], '_non_trainable_weights': [], '_losses': [], '_updates': [], '_per_input_losses': {}, '_per_input_updates': {}, '_built': True, 'inbound_nodes': [], 'outbound_nodes': [], 'name': 'dense_4', 'trainable': True, '_initial_weights': None, 'units': 10, 'activation': , 'use_bias': True, 'kernel_initializer': , 'bias_initializer': , 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None, 'kernel': , 'bias': }\n", + "LAYER_CHECK_COLUMNS ('dense_4', , (None, 128), 129, 129)\n", + "KERAS_GENERATE_LAYER_START \n", + "ABSTRACT_GENERATE_CTE layer_dense_4 layer_dense_4 11\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ABSTRACT_GENERATE_CTE score_soft_max_step1 score_soft_max_step1 21\n", + "ABSTRACT_GENERATE_CTE score_class_union_soft score_class_union_soft 3\n", + "ABSTRACT_GENERATE_CTE score_soft_max score_soft_max 1\n", + "['score_soft_max.KEY_7_7', 'score_soft_max.\"Score_0\"', 'score_soft_max.\"exp_Score_0\"', 'score_soft_max.\"Score_1\"', 'score_soft_max.\"exp_Score_1\"', 'score_soft_max.\"Score_2\"', 'score_soft_max.\"exp_Score_2\"', 'score_soft_max.\"Score_3\"', 'score_soft_max.\"exp_Score_3\"', 'score_soft_max.\"Score_4\"', 'score_soft_max.\"exp_Score_4\"', 'score_soft_max.\"Score_5\"', 'score_soft_max.\"exp_Score_5\"', 'score_soft_max.\"Score_6\"', 'score_soft_max.\"exp_Score_6\"', 'score_soft_max.\"Score_7\"', 'score_soft_max.\"exp_Score_7\"', 'score_soft_max.\"Score_8\"', 'score_soft_max.\"exp_Score_8\"', 'score_soft_max.\"Score_9\"', 'score_soft_max.\"exp_Score_9\"', 'score_soft_max.KEY_7_7_sum', 'score_soft_max.\"sum_ExpScore\"']\n", + "ABSTRACT_GENERATE_CTE layer_softmax layer_softmax 11\n", + "ABSTRACT_GENERATE_CTE orig_cte orig_cte 34\n", + "ABSTRACT_GENERATE_CTE score_class_union score_class_union 5\n", + "ABSTRACT_GENERATE_CTE score_max score_max 1\n", + "ABSTRACT_GENERATE_CTE union_with_max union_with_max 1\n", + "['KEY_u', 'class', 'LogProba', 'Proba', 'Score', 'KEY', 'Score_0', 'Score_1', 'Score_2', 'Score_3', 'Score_4', 'Score_5', 'Score_6', 'Score_7', 'Score_8', 'Score_9', 'Proba_0', 'Proba_1', 'Proba_2', 'Proba_3', 'Proba_4', 'Proba_5', 'Proba_6', 'Proba_7', 'Proba_8', 'Proba_9', 'LogProba_0', 'LogProba_1', 'LogProba_2', 'LogProba_3', 'LogProba_4', 'LogProba_5', 'LogProba_6', 'LogProba_7', 'LogProba_8', 'LogProba_9', 'Decision', 'DecisionProba', 'KEY_m', 'max_Proba']\n", + "ABSTRACT_GENERATE_CTE arg_max_cte arg_max_cte 1\n", + "KERAS_CODE_GENERATION_END \n" + ] + } + ], + "source": [ + "lMetaData = {}\n", + "NC = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n", + "lMetaData['features'] = [\"X_\" + str(x+1) for x in range(0 , NC)]\n", + "\n", + "lMetaData[\"targets\"] = ['TGT']\n", + "lMetaData['primary_key'] = 'KEY'\n", + "lMetaData['table'] = 'mnist'\n", + "\n", + " \n", + "lSQL = test_sql_gen(clf , lMetaData)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WITH keras_input AS \n", + "(SELECT \"ADS\".\"KEY\" AS \"KEY\", \"ADS\".\"X_1\" AS \"X_1\", \"ADS\".\"X_2\" AS \"X_2\", \"ADS\".\"X_3\" AS \"X_3\", \"ADS\".\"X_4\" AS \"X_4\", \"ADS\".\"X_5\" AS \"X_5\", \"ADS\".\"X_6\" AS \"X_6\", \"ADS\".\"X_7\" AS \"X_7\", \"ADS\".\"X_8\" AS \"X_8\", \"ADS\".\"X_9\" AS \"X_9\", \"ADS\".\"X_10\" AS \"X_10\", \"ADS\".\"X_11\" AS \"X_11\", \"ADS\".\"X_12\" AS \"X_12\", \"ADS\".\"X_13\" AS \"X_13\", \"ADS\".\"X_14\" AS \"X_14\", \"ADS\".\"X_15\" AS \"X_15\", \"ADS\".\"X_16\" AS \"X_16\", \"ADS\".\"X_17\" AS \"X_17\", \"ADS\".\"X_18\" AS \"X_18\", \"ADS\".\"X_19\" AS \"X_19\", \"ADS\".\"X_20\" AS \"X_20\", \"ADS\".\"X_21\" AS \"X_21\", \"ADS\".\"X_22\" AS \"X_22\", \"ADS\".\"X_23\" AS \"X_23\", \"ADS\".\"X_24\" AS \"X_24\", \"ADS\".\"X_25\" AS \"X_25\", \"ADS\".\"X_26\" AS \"X_26\", \"ADS\".\"X_27\" AS \"X_27\", \"ADS\".\"X_28\" AS \"X_28\", \"ADS\".\"X_29\" AS \"X_29\", \"ADS\".\"X_30\" AS \"X_30\", \"ADS\".\"X_31\" AS \"X_31\", \"ADS\".\"X_32\" AS \"X_32\", \"ADS\".\"X_33\" AS \"X_33\", \"ADS\".\"X_34\" AS \"X_34\", \"ADS\".\"X_35\" AS \"X_35\", \"ADS\".\"X_36\" AS \"X_36\", \"ADS\".\"X_37\" AS \"X_37\", \"ADS\".\"X_38\" AS \"X_38\", \"ADS\".\"X_39\" AS \"X_39\", \"ADS\".\"X_40\" AS \"X_40\", \"ADS\".\"X_41\" AS \"X_41\", \"ADS\".\"X_42\" AS \"X_42\", \"ADS\".\"X_43\" AS \"X_43\", \"ADS\".\"X_44\" AS \"X_44\", \"ADS\".\"X_45\" AS \"X_45\", \"ADS\".\"X_46\" AS \"X_46\", \"ADS\".\"X_47\" AS \"X_47\", \"ADS\".\"X_48\" AS \"X_48\", \"ADS\".\"X_49\" AS \"X_49\", \"ADS\".\"X_50\" AS \"X_50\", \"ADS\".\"X_51\" AS \"X_51\", \"ADS\".\"X_52\" AS \"X_52\", \"ADS\".\"X_53\" AS \"X_53\", \"ADS\".\"X_54\" AS \"X_54\", \"ADS\".\"X_55\" AS \"X_55\", \"ADS\".\"X_56\" AS \"X_56\", \"ADS\".\"X_57\" AS \"X_57\", \"ADS\".\"X_58\" AS \"X_58\", \"ADS\".\"X_59\" AS \"X_59\", \"ADS\".\"X_60\" AS \"X_60\", \"ADS\".\"X_61\" AS \"X_61\", \"ADS\".\"X_62\" AS \"X_62\", \"ADS\".\"X_63\" AS \"X_63\", \"ADS\".\"X_64\" AS \"X_64\", \"ADS\".\"X_65\" AS \"X_65\", \"ADS\".\"X_66\" AS \"X_66\", \"ADS\".\"X_67\" AS \"X_67\", \"ADS\".\"X_68\" AS \"X_68\", \"ADS\".\"X_69\" AS \"X_69\", \"ADS\".\"X_70\" AS \"X_70\", \"ADS\".\"X_71\" AS \"X_71\", \"ADS\".\"X_72\" AS \"X_72\", \"ADS\".\"X_73\" AS \"X_73\", \"ADS\".\"X_74\" AS \"X_74\", \"ADS\".\"X_75\" AS \"X_75\", \"ADS\".\"X_76\" AS \"X_76\", \"ADS\".\"X_77\" AS \"X_77\", \"ADS\".\"X_78\" AS \"X_78\", \"ADS\".\"X_79\" AS \"X_79\", \"ADS\".\"X_80\" AS \"X_80\", \"ADS\".\"X_81\" AS \"X_81\", \"ADS\".\"X_82\" AS \"X_82\", \"ADS\".\"X_83\" AS \"X_83\", \"ADS\".\"X_84\" AS \"X_84\", \"ADS\".\"X_85\" AS \"X_85\", \"ADS\".\"X_86\" AS \"X_86\", \"ADS\".\"X_87\" AS \"X_87\", \"ADS\".\"X_88\" AS \"X_88\", \"ADS\".\"X_89\" AS \"X_89\", \"ADS\".\"X_90\" AS \"X_90\", \"ADS\".\"X_91\" AS \"X_91\", \"ADS\".\"X_92\" AS \"X_92\", \"ADS\".\"X_93\" AS \"X_93\", \"ADS\".\"X_94\" AS \"X_94\", \"ADS\".\"X_95\" AS \"X_95\", \"ADS\".\"X_96\" AS \"X_96\", \"ADS\".\"X_97\" AS \"X_97\", \"ADS\".\"X_98\" AS \"X_98\", \"ADS\".\"X_99\" AS \"X_99\", \"ADS\".\"X_100\" AS \"X_100\", \"ADS\".\"X_101\" AS \"X_101\", \"ADS\".\"X_102\" AS \"X_102\", \"ADS\".\"X_103\" AS \"X_103\", \"ADS\".\"X_104\" AS \"X_104\", \"ADS\".\"X_105\" AS \"X_105\", \"ADS\".\"X_106\" AS \"X_106\", \"ADS\".\"X_107\" AS \"X_107\", \"ADS\".\"X_108\" AS \"X_108\", \"ADS\".\"X_109\" AS \"X_109\", \"ADS\".\"X_110\" AS \"X_110\", \"ADS\".\"X_111\" AS \"X_111\", \"ADS\".\"X_112\" AS \"X_112\", \"ADS\".\"X_113\" AS \"X_113\", \"ADS\".\"X_114\" AS \"X_114\", \"ADS\".\"X_115\" AS \"X_115\", \"ADS\".\"X_116\" AS \"X_116\", \"ADS\".\"X_117\" AS \"X_117\", \"ADS\".\"X_118\" AS \"X_118\", \"ADS\".\"X_119\" AS \"X_119\", \"ADS\".\"X_120\" AS \"X_120\", \"ADS\".\"X_121\" AS \"X_121\", \"ADS\".\"X_122\" AS \"X_122\", \"ADS\".\"X_123\" AS \"X_123\", \"ADS\".\"X_124\" AS \"X_124\", \"ADS\".\"X_125\" AS \"X_125\", \"ADS\".\"X_126\" AS \"X_126\", \"ADS\".\"X_127\" AS \"X_127\", \"ADS\".\"X_128\" AS \"X_128\", \"ADS\".\"X_129\" AS \"X_129\", \"ADS\".\"X_130\" AS \"X_130\", \"ADS\".\"X_131\" AS \"X_131\", \"ADS\".\"X_132\" AS \"X_132\", \"ADS\".\"X_133\" AS \"X_133\", \"ADS\".\"X_134\" AS \"X_134\", \"ADS\".\"X_135\" AS \"X_135\", \"ADS\".\"X_136\" AS \"X_136\", \"ADS\".\"X_137\" AS \"X_137\", \"ADS\".\"X_138\" AS \"X_138\", \"ADS\".\"X_139\" AS \"X_139\", \"ADS\".\"X_140\" AS \"X_140\", \"ADS\".\"X_141\" AS \"X_141\", \"ADS\".\"X_142\" AS \"X_142\", \"ADS\".\"X_143\" AS \"X_143\", \"ADS\".\"X_144\" AS \"X_144\", \"ADS\".\"X_145\" AS \"X_145\", \"ADS\".\"X_146\" AS \"X_146\", \"ADS\".\"X_147\" AS \"X_147\", \"ADS\".\"X_148\" AS \"X_148\", \"ADS\".\"X_149\" AS \"X_149\", \"ADS\".\"X_150\" AS \"X_150\", \"ADS\".\"X_151\" AS \"X_151\", \"ADS\".\"X_152\" AS \"X_152\", \"ADS\".\"X_153\" AS \"X_153\", \"ADS\".\"X_154\" AS \"X_154\", \"ADS\".\"X_155\" AS \"X_155\", \"ADS\".\"X_156\" AS \"X_156\", \"ADS\".\"X_157\" AS \"X_157\", \"ADS\".\"X_158\" AS \"X_158\", \"ADS\".\"X_159\" AS \"X_159\", \"ADS\".\"X_160\" AS \"X_160\", \"ADS\".\"X_161\" AS \"X_161\", \"ADS\".\"X_162\" AS \"X_162\", \"ADS\".\"X_163\" AS \"X_163\", \"ADS\".\"X_164\" AS \"X_164\", \"ADS\".\"X_165\" AS \"X_165\", \"ADS\".\"X_166\" AS \"X_166\", \"ADS\".\"X_167\" AS \"X_167\", \"ADS\".\"X_168\" AS \"X_168\", \"ADS\".\"X_169\" AS \"X_169\", \"ADS\".\"X_170\" AS \"X_170\", \"ADS\".\"X_171\" AS \"X_171\", \"ADS\".\"X_172\" AS \"X_172\", \"ADS\".\"X_173\" AS \"X_173\", \"ADS\".\"X_174\" AS \"X_174\", \"ADS\".\"X_175\" AS \"X_175\", \"ADS\".\"X_176\" AS \"X_176\", \"ADS\".\"X_177\" AS \"X_177\", \"ADS\".\"X_178\" AS \"X_178\", \"ADS\".\"X_179\" AS \"X_179\", \"ADS\".\"X_180\" AS \"X_180\", \"ADS\".\"X_181\" AS \"X_181\", \"ADS\".\"X_182\" AS \"X_182\", \"ADS\".\"X_183\" AS \"X_183\", \"ADS\".\"X_184\" AS \"X_184\", \"ADS\".\"X_185\" AS \"X_185\", \"ADS\".\"X_186\" AS \"X_186\", \"ADS\".\"X_187\" AS \"X_187\", \"ADS\".\"X_188\" AS \"X_188\", \"ADS\".\"X_189\" AS \"X_189\", \"ADS\".\"X_190\" AS \"X_190\", \"ADS\".\"X_191\" AS \"X_191\", \"ADS\".\"X_192\" AS \"X_192\", \"ADS\".\"X_193\" AS \"X_193\", \"ADS\".\"X_194\" AS \"X_194\", \"ADS\".\"X_195\" AS \"X_195\", \"ADS\".\"X_196\" AS \"X_196\", \"ADS\".\"X_197\" AS \"X_197\", \"ADS\".\"X_198\" AS \"X_198\", \"ADS\".\"X_199\" AS \"X_199\", \"ADS\".\"X_200\" AS \"X_200\", \"ADS\".\"X_201\" AS \"X_201\", \"ADS\".\"X_202\" AS \"X_202\", \"ADS\".\"X_203\" AS \"X_203\", \"ADS\".\"X_204\" AS \"X_204\", \"ADS\".\"X_205\" AS \"X_205\", \"ADS\".\"X_206\" AS \"X_206\", \"ADS\".\"X_207\" AS \"X_207\", \"ADS\".\"X_208\" AS \"X_208\", \"ADS\".\"X_209\" AS \"X_209\", \"ADS\".\"X_210\" AS \"X_210\", \"ADS\".\"X_211\" AS \"X_211\", \"ADS\".\"X_212\" AS \"X_212\", \"ADS\".\"X_213\" AS \"X_213\", \"ADS\".\"X_214\" AS \"X_214\", \"ADS\".\"X_215\" AS \"X_215\", \"ADS\".\"X_216\" AS \"X_216\", \"ADS\".\"X_217\" AS \"X_217\", \"ADS\".\"X_218\" AS \"X_218\", \"ADS\".\"X_219\" AS \"X_219\", \"ADS\".\"X_220\" AS \"X_220\", \"ADS\".\"X_221\" AS \"X_221\", \"ADS\".\"X_222\" AS \"X_222\", \"ADS\".\"X_223\" AS \"X_223\", \"ADS\".\"X_224\" AS \"X_224\", \"ADS\".\"X_225\" AS \"X_225\", \"ADS\".\"X_226\" AS \"X_226\", \"ADS\".\"X_227\" AS \"X_227\", \"ADS\".\"X_228\" AS \"X_228\", \"ADS\".\"X_229\" AS \"X_229\", \"ADS\".\"X_230\" AS \"X_230\", \"ADS\".\"X_231\" AS \"X_231\", \"ADS\".\"X_232\" AS \"X_232\", \"ADS\".\"X_233\" AS \"X_233\", \"ADS\".\"X_234\" AS \"X_234\", \"ADS\".\"X_235\" AS \"X_235\", \"ADS\".\"X_236\" AS \"X_236\", \"ADS\".\"X_237\" AS \"X_237\", \"ADS\".\"X_238\" AS \"X_238\", \"ADS\".\"X_239\" AS \"X_239\", \"ADS\".\"X_240\" AS \"X_240\", \"ADS\".\"X_241\" AS \"X_241\", \"ADS\".\"X_242\" AS \"X_242\", \"ADS\".\"X_243\" AS \"X_243\", \"ADS\".\"X_244\" AS \"X_244\", \"ADS\".\"X_245\" AS \"X_245\", \"ADS\".\"X_246\" AS \"X_246\", \"ADS\".\"X_247\" AS \"X_247\", \"ADS\".\"X_248\" AS \"X_248\", \"ADS\".\"X_249\" AS \"X_249\", \"ADS\".\"X_250\" AS \"X_250\", \"ADS\".\"X_251\" AS \"X_251\", \"ADS\".\"X_252\" AS \"X_252\", \"ADS\".\"X_253\" AS \"X_253\", \"ADS\".\"X_254\" AS \"X_254\", \"ADS\".\"X_255\" AS \"X_255\", \"ADS\".\"X_256\" AS \"X_256\", \"ADS\".\"X_257\" AS \"X_257\", \"ADS\".\"X_258\" AS \"X_258\", \"ADS\".\"X_259\" AS \"X_259\", \"ADS\".\"X_260\" AS \"X_260\", \"ADS\".\"X_261\" AS \"X_261\", \"ADS\".\"X_262\" AS \"X_262\", \"ADS\".\"X_263\" AS \"X_263\", \"ADS\".\"X_264\" AS \"X_264\", \"ADS\".\"X_265\" AS \"X_265\", \"ADS\".\"X_266\" AS \"X_266\", \"ADS\".\"X_267\" AS \"X_267\", \"ADS\".\"X_268\" AS \"X_268\", \"ADS\".\"X_269\" AS \"X_269\", \"ADS\".\"X_270\" AS \"X_270\", \"ADS\".\"X_271\" AS \"X_271\", \"ADS\".\"X_272\" AS \"X_272\", \"ADS\".\"X_273\" AS \"X_273\", \"ADS\".\"X_274\" AS \"X_274\", \"ADS\".\"X_275\" AS \"X_275\", \"ADS\".\"X_276\" AS \"X_276\", \"ADS\".\"X_277\" AS \"X_277\", \"ADS\".\"X_278\" AS \"X_278\", \"ADS\".\"X_279\" AS \"X_279\", \"ADS\".\"X_280\" AS \"X_280\", \"ADS\".\"X_281\" AS \"X_281\", \"ADS\".\"X_282\" AS \"X_282\", \"ADS\".\"X_283\" AS \"X_283\", \"ADS\".\"X_284\" AS \"X_284\", \"ADS\".\"X_285\" AS \"X_285\", \"ADS\".\"X_286\" AS \"X_286\", \"ADS\".\"X_287\" AS \"X_287\", \"ADS\".\"X_288\" AS \"X_288\", \"ADS\".\"X_289\" AS \"X_289\", \"ADS\".\"X_290\" AS \"X_290\", \"ADS\".\"X_291\" AS \"X_291\", \"ADS\".\"X_292\" AS \"X_292\", \"ADS\".\"X_293\" AS \"X_293\", \"ADS\".\"X_294\" AS \"X_294\", \"ADS\".\"X_295\" AS \"X_295\", \"ADS\".\"X_296\" AS \"X_296\", \"ADS\".\"X_297\" AS \"X_297\", \"ADS\".\"X_298\" AS \"X_298\", \"ADS\".\"X_299\" AS \"X_299\", \"ADS\".\"X_300\" AS \"X_300\", \"ADS\".\"X_301\" AS \"X_301\", \"ADS\".\"X_302\" AS \"X_302\", \"ADS\".\"X_303\" AS \"X_303\", \"ADS\".\"X_304\" AS \"X_304\", \"ADS\".\"X_305\" AS \"X_305\", \"ADS\".\"X_306\" AS \"X_306\", \"ADS\".\"X_307\" AS \"X_307\", \"ADS\".\"X_308\" AS \"X_308\", \"ADS\".\"X_309\" AS \"X_309\", \"ADS\".\"X_310\" AS \"X_310\", \"ADS\".\"X_311\" AS \"X_311\", \"ADS\".\"X_312\" AS \"X_312\", \"ADS\".\"X_313\" AS \"X_313\", \"ADS\".\"X_314\" AS \"X_314\", \"ADS\".\"X_315\" AS \"X_315\", \"ADS\".\"X_316\" AS \"X_316\", \"ADS\".\"X_317\" AS \"X_317\", \"ADS\".\"X_318\" AS \"X_318\", \"ADS\".\"X_319\" AS \"X_319\", \"ADS\".\"X_320\" AS \"X_320\", \"ADS\".\"X_321\" AS \"X_321\", \"ADS\".\"X_322\" AS \"X_322\", \"ADS\".\"X_323\" AS \"X_323\", \"ADS\".\"X_324\" AS \"X_324\", \"ADS\".\"X_325\" AS \"X_325\", \"ADS\".\"X_326\" AS \"X_326\", \"ADS\".\"X_327\" AS \"X_327\", \"ADS\".\"X_328\" AS \"X_328\", \"ADS\".\"X_329\" AS \"X_329\", \"ADS\".\"X_330\" AS \"X_330\", \"ADS\".\"X_331\" AS \"X_331\", \"ADS\".\"X_332\" AS \"X_332\", \"ADS\".\"X_333\" AS \"X_333\", \"ADS\".\"X_334\" AS \"X_334\", \"ADS\".\"X_335\" AS \"X_335\", \"ADS\".\"X_336\" AS \"X_336\", \"ADS\".\"X_337\" AS \"X_337\", \"ADS\".\"X_338\" AS \"X_338\", \"ADS\".\"X_339\" AS \"X_339\", \"ADS\".\"X_340\" AS \"X_340\", \"ADS\".\"X_341\" AS \"X_341\", \"ADS\".\"X_342\" AS \"X_342\", \"ADS\".\"X_343\" AS \"X_343\", \"ADS\".\"X_344\" AS \"X_344\", \"ADS\".\"X_345\" AS \"X_345\", \"ADS\".\"X_346\" AS \"X_346\", \"ADS\".\"X_347\" AS \"X_347\", \"ADS\".\"X_348\" AS \"X_348\", \"ADS\".\"X_349\" AS \"X_349\", \"ADS\".\"X_350\" AS \"X_350\", \"ADS\".\"X_351\" AS \"X_351\", \"ADS\".\"X_352\" AS \"X_352\", \"ADS\".\"X_353\" AS \"X_353\", \"ADS\".\"X_354\" AS \"X_354\", \"ADS\".\"X_355\" AS \"X_355\", \"ADS\".\"X_356\" AS \"X_356\", \"ADS\".\"X_357\" AS \"X_357\", \"ADS\".\"X_358\" AS \"X_358\", \"ADS\".\"X_359\" AS \"X_359\", \"ADS\".\"X_360\" AS \"X_360\", \"ADS\".\"X_361\" AS \"X_361\", \"ADS\".\"X_362\" AS \"X_362\", \"ADS\".\"X_363\" AS \"X_363\", \"ADS\".\"X_364\" AS \"X_364\", \"ADS\".\"X_365\" AS \"X_365\", \"ADS\".\"X_366\" AS \"X_366\", \"ADS\".\"X_367\" AS \"X_367\", \"ADS\".\"X_368\" AS \"X_368\", \"ADS\".\"X_369\" AS \"X_369\", \"ADS\".\"X_370\" AS \"X_370\", \"ADS\".\"X_371\" AS \"X_371\", \"ADS\".\"X_372\" AS \"X_372\", \"ADS\".\"X_373\" AS \"X_373\", \"ADS\".\"X_374\" AS \"X_374\", 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\"ADS\".\"X_530\" AS \"X_530\", \"ADS\".\"X_531\" AS \"X_531\", \"ADS\".\"X_532\" AS \"X_532\", \"ADS\".\"X_533\" AS \"X_533\", \"ADS\".\"X_534\" AS \"X_534\", \"ADS\".\"X_535\" AS \"X_535\", \"ADS\".\"X_536\" AS \"X_536\", \"ADS\".\"X_537\" AS \"X_537\", \"ADS\".\"X_538\" AS \"X_538\", \"ADS\".\"X_539\" AS \"X_539\", \"ADS\".\"X_540\" AS \"X_540\", \"ADS\".\"X_541\" AS \"X_541\", \"ADS\".\"X_542\" AS \"X_542\", \"ADS\".\"X_543\" AS \"X_543\", \"ADS\".\"X_544\" AS \"X_544\", \"ADS\".\"X_545\" AS \"X_545\", \"ADS\".\"X_546\" AS \"X_546\", \"ADS\".\"X_547\" AS \"X_547\", \"ADS\".\"X_548\" AS \"X_548\", \"ADS\".\"X_549\" AS \"X_549\", \"ADS\".\"X_550\" AS \"X_550\", \"ADS\".\"X_551\" AS \"X_551\", \"ADS\".\"X_552\" AS \"X_552\", \"ADS\".\"X_553\" AS \"X_553\", \"ADS\".\"X_554\" AS \"X_554\", \"ADS\".\"X_555\" AS \"X_555\", \"ADS\".\"X_556\" AS \"X_556\", \"ADS\".\"X_557\" AS \"X_557\", \"ADS\".\"X_558\" AS \"X_558\", \"ADS\".\"X_559\" AS \"X_559\", \"ADS\".\"X_560\" AS \"X_560\", \"ADS\".\"X_561\" AS \"X_561\", \"ADS\".\"X_562\" AS \"X_562\", \"ADS\".\"X_563\" AS \"X_563\", \"ADS\".\"X_564\" AS \"X_564\", \"ADS\".\"X_565\" AS \"X_565\", \"ADS\".\"X_566\" AS \"X_566\", \"ADS\".\"X_567\" AS \"X_567\", \"ADS\".\"X_568\" AS \"X_568\", \"ADS\".\"X_569\" AS \"X_569\", \"ADS\".\"X_570\" AS \"X_570\", \"ADS\".\"X_571\" AS \"X_571\", \"ADS\".\"X_572\" AS \"X_572\", \"ADS\".\"X_573\" AS \"X_573\", \"ADS\".\"X_574\" AS \"X_574\", \"ADS\".\"X_575\" AS \"X_575\", \"ADS\".\"X_576\" AS \"X_576\", \"ADS\".\"X_577\" AS \"X_577\", \"ADS\".\"X_578\" AS \"X_578\", \"ADS\".\"X_579\" AS \"X_579\", \"ADS\".\"X_580\" AS \"X_580\", \"ADS\".\"X_581\" AS \"X_581\", \"ADS\".\"X_582\" AS \"X_582\", \"ADS\".\"X_583\" AS \"X_583\", \"ADS\".\"X_584\" AS \"X_584\", \"ADS\".\"X_585\" AS \"X_585\", \"ADS\".\"X_586\" AS \"X_586\", \"ADS\".\"X_587\" AS \"X_587\", \"ADS\".\"X_588\" AS \"X_588\", \"ADS\".\"X_589\" AS \"X_589\", \"ADS\".\"X_590\" AS \"X_590\", \"ADS\".\"X_591\" AS \"X_591\", \"ADS\".\"X_592\" AS \"X_592\", \"ADS\".\"X_593\" AS \"X_593\", \"ADS\".\"X_594\" AS \"X_594\", \"ADS\".\"X_595\" AS \"X_595\", \"ADS\".\"X_596\" AS \"X_596\", \"ADS\".\"X_597\" AS \"X_597\", \"ADS\".\"X_598\" AS \"X_598\", \"ADS\".\"X_599\" AS \"X_599\", \"ADS\".\"X_600\" AS \"X_600\", \"ADS\".\"X_601\" AS \"X_601\", \"ADS\".\"X_602\" AS \"X_602\", \"ADS\".\"X_603\" AS \"X_603\", \"ADS\".\"X_604\" AS \"X_604\", \"ADS\".\"X_605\" AS \"X_605\", \"ADS\".\"X_606\" AS \"X_606\", \"ADS\".\"X_607\" AS \"X_607\", \"ADS\".\"X_608\" AS \"X_608\", \"ADS\".\"X_609\" AS \"X_609\", \"ADS\".\"X_610\" AS \"X_610\", \"ADS\".\"X_611\" AS \"X_611\", \"ADS\".\"X_612\" AS \"X_612\", \"ADS\".\"X_613\" AS \"X_613\", \"ADS\".\"X_614\" AS \"X_614\", \"ADS\".\"X_615\" AS \"X_615\", \"ADS\".\"X_616\" AS \"X_616\", \"ADS\".\"X_617\" AS \"X_617\", \"ADS\".\"X_618\" AS \"X_618\", \"ADS\".\"X_619\" AS \"X_619\", \"ADS\".\"X_620\" AS \"X_620\", \"ADS\".\"X_621\" AS \"X_621\", \"ADS\".\"X_622\" AS \"X_622\", \"ADS\".\"X_623\" AS \"X_623\", \"ADS\".\"X_624\" AS \"X_624\", \"ADS\".\"X_625\" AS \"X_625\", \"ADS\".\"X_626\" AS \"X_626\", \"ADS\".\"X_627\" AS \"X_627\", \"ADS\".\"X_628\" AS \"X_628\", \"ADS\".\"X_629\" AS \"X_629\", \"ADS\".\"X_630\" AS \"X_630\", \"ADS\".\"X_631\" AS \"X_631\", \"ADS\".\"X_632\" AS \"X_632\", \"ADS\".\"X_633\" AS \"X_633\", \"ADS\".\"X_634\" AS \"X_634\", \"ADS\".\"X_635\" AS \"X_635\", \"ADS\".\"X_636\" AS \"X_636\", \"ADS\".\"X_637\" AS \"X_637\", \"ADS\".\"X_638\" AS \"X_638\", \"ADS\".\"X_639\" AS \"X_639\", \"ADS\".\"X_640\" AS \"X_640\", \"ADS\".\"X_641\" AS \"X_641\", \"ADS\".\"X_642\" AS \"X_642\", \"ADS\".\"X_643\" AS \"X_643\", \"ADS\".\"X_644\" AS \"X_644\", \"ADS\".\"X_645\" AS \"X_645\", \"ADS\".\"X_646\" AS \"X_646\", \"ADS\".\"X_647\" AS \"X_647\", \"ADS\".\"X_648\" AS \"X_648\", \"ADS\".\"X_649\" AS \"X_649\", \"ADS\".\"X_650\" AS \"X_650\", \"ADS\".\"X_651\" AS \"X_651\", \"ADS\".\"X_652\" AS \"X_652\", \"ADS\".\"X_653\" AS \"X_653\", \"ADS\".\"X_654\" AS \"X_654\", \"ADS\".\"X_655\" AS \"X_655\", \"ADS\".\"X_656\" AS \"X_656\", \"ADS\".\"X_657\" AS \"X_657\", \"ADS\".\"X_658\" AS \"X_658\", \"ADS\".\"X_659\" AS \"X_659\", \"ADS\".\"X_660\" AS \"X_660\", \"ADS\".\"X_661\" AS \"X_661\", \"ADS\".\"X_662\" AS \"X_662\", \"ADS\".\"X_663\" AS \"X_663\", \"ADS\".\"X_664\" AS \"X_664\", \"ADS\".\"X_665\" AS \"X_665\", \"ADS\".\"X_666\" AS \"X_666\", \"ADS\".\"X_667\" AS \"X_667\", \"ADS\".\"X_668\" AS \"X_668\", \"ADS\".\"X_669\" AS \"X_669\", \"ADS\".\"X_670\" AS \"X_670\", \"ADS\".\"X_671\" AS \"X_671\", \"ADS\".\"X_672\" AS \"X_672\", \"ADS\".\"X_673\" AS \"X_673\", \"ADS\".\"X_674\" AS \"X_674\", \"ADS\".\"X_675\" AS \"X_675\", \"ADS\".\"X_676\" AS \"X_676\", \"ADS\".\"X_677\" AS \"X_677\", \"ADS\".\"X_678\" AS \"X_678\", \"ADS\".\"X_679\" AS \"X_679\", \"ADS\".\"X_680\" AS \"X_680\", \"ADS\".\"X_681\" AS \"X_681\", \"ADS\".\"X_682\" AS \"X_682\", \"ADS\".\"X_683\" AS \"X_683\", \"ADS\".\"X_684\" AS \"X_684\", \"ADS\".\"X_685\" AS \"X_685\", \"ADS\".\"X_686\" AS \"X_686\", \"ADS\".\"X_687\" AS \"X_687\", \"ADS\".\"X_688\" AS \"X_688\", \"ADS\".\"X_689\" AS \"X_689\", \"ADS\".\"X_690\" AS \"X_690\", \"ADS\".\"X_691\" AS \"X_691\", \"ADS\".\"X_692\" AS \"X_692\", \"ADS\".\"X_693\" AS \"X_693\", \"ADS\".\"X_694\" AS \"X_694\", \"ADS\".\"X_695\" AS \"X_695\", \"ADS\".\"X_696\" AS \"X_696\", \"ADS\".\"X_697\" AS \"X_697\", \"ADS\".\"X_698\" AS \"X_698\", \"ADS\".\"X_699\" AS \"X_699\", \"ADS\".\"X_700\" AS \"X_700\", \"ADS\".\"X_701\" AS \"X_701\", \"ADS\".\"X_702\" AS \"X_702\", \"ADS\".\"X_703\" AS \"X_703\", \"ADS\".\"X_704\" AS \"X_704\", \"ADS\".\"X_705\" AS \"X_705\", \"ADS\".\"X_706\" AS \"X_706\", \"ADS\".\"X_707\" AS \"X_707\", \"ADS\".\"X_708\" AS \"X_708\", \"ADS\".\"X_709\" AS \"X_709\", \"ADS\".\"X_710\" AS \"X_710\", \"ADS\".\"X_711\" AS \"X_711\", \"ADS\".\"X_712\" AS \"X_712\", \"ADS\".\"X_713\" AS \"X_713\", \"ADS\".\"X_714\" AS \"X_714\", \"ADS\".\"X_715\" AS \"X_715\", \"ADS\".\"X_716\" AS \"X_716\", \"ADS\".\"X_717\" AS \"X_717\", \"ADS\".\"X_718\" AS \"X_718\", \"ADS\".\"X_719\" AS \"X_719\", \"ADS\".\"X_720\" AS \"X_720\", \"ADS\".\"X_721\" AS \"X_721\", \"ADS\".\"X_722\" AS \"X_722\", \"ADS\".\"X_723\" AS \"X_723\", \"ADS\".\"X_724\" AS \"X_724\", \"ADS\".\"X_725\" AS \"X_725\", \"ADS\".\"X_726\" AS \"X_726\", \"ADS\".\"X_727\" AS \"X_727\", \"ADS\".\"X_728\" AS \"X_728\", \"ADS\".\"X_729\" AS \"X_729\", \"ADS\".\"X_730\" AS \"X_730\", \"ADS\".\"X_731\" AS \"X_731\", \"ADS\".\"X_732\" AS \"X_732\", \"ADS\".\"X_733\" AS \"X_733\", \"ADS\".\"X_734\" AS \"X_734\", \"ADS\".\"X_735\" AS \"X_735\", \"ADS\".\"X_736\" AS \"X_736\", \"ADS\".\"X_737\" AS \"X_737\", \"ADS\".\"X_738\" AS \"X_738\", \"ADS\".\"X_739\" AS \"X_739\", \"ADS\".\"X_740\" AS \"X_740\", \"ADS\".\"X_741\" AS \"X_741\", \"ADS\".\"X_742\" AS \"X_742\", \"ADS\".\"X_743\" AS \"X_743\", \"ADS\".\"X_744\" AS \"X_744\", \"ADS\".\"X_745\" AS \"X_745\", \"ADS\".\"X_746\" AS \"X_746\", \"ADS\".\"X_747\" AS \"X_747\", \"ADS\".\"X_748\" AS \"X_748\", \"ADS\".\"X_749\" AS \"X_749\", \"ADS\".\"X_750\" AS \"X_750\", \"ADS\".\"X_751\" AS \"X_751\", \"ADS\".\"X_752\" AS \"X_752\", \"ADS\".\"X_753\" AS \"X_753\", \"ADS\".\"X_754\" AS \"X_754\", \"ADS\".\"X_755\" AS \"X_755\", \"ADS\".\"X_756\" AS \"X_756\", \"ADS\".\"X_757\" AS \"X_757\", \"ADS\".\"X_758\" AS \"X_758\", \"ADS\".\"X_759\" AS \"X_759\", \"ADS\".\"X_760\" AS \"X_760\", \"ADS\".\"X_761\" AS \"X_761\", \"ADS\".\"X_762\" AS \"X_762\", \"ADS\".\"X_763\" AS \"X_763\", \"ADS\".\"X_764\" AS \"X_764\", \"ADS\".\"X_765\" AS \"X_765\", \"ADS\".\"X_766\" AS \"X_766\", \"ADS\".\"X_767\" AS \"X_767\", \"ADS\".\"X_768\" AS \"X_768\", \"ADS\".\"X_769\" AS \"X_769\", \"ADS\".\"X_770\" AS \"X_770\", \"ADS\".\"X_771\" AS \"X_771\", \"ADS\".\"X_772\" AS \"X_772\", \"ADS\".\"X_773\" AS \"X_773\", \"ADS\".\"X_774\" AS \"X_774\", \"ADS\".\"X_775\" AS \"X_775\", \"ADS\".\"X_776\" AS \"X_776\", \"ADS\".\"X_777\" AS \"X_777\", \"ADS\".\"X_778\" AS \"X_778\", \"ADS\".\"X_779\" AS \"X_779\", \"ADS\".\"X_780\" AS \"X_780\", \"ADS\".\"X_781\" AS \"X_781\", \"ADS\".\"X_782\" AS \"X_782\", \"ADS\".\"X_783\" AS \"X_783\", \"ADS\".\"X_784\" AS \"X_784\" \n", + "FROM mnist AS \"ADS\"), \n", + "\"layer_conv2d_2_Filter0\" AS \n", + "(SELECT keras_input.\"KEY\" AS \"KEY\", 0.017863758 + keras_input.\"X_1\" * 0.12594759464263916 + keras_input.\"X_2\" * 0.6201684474945068 + keras_input.\"X_3\" * 0.48617687821388245 + keras_input.\"X_29\" * -0.1793716996908188 + keras_input.\"X_30\" * -0.1397959291934967 + keras_input.\"X_31\" * -0.02336321771144867 + keras_input.\"X_57\" * -0.8183432221412659 + keras_input.\"X_58\" * -0.23505675792694092 + keras_input.\"X_59\" * -0.31184646487236023 AS output_0_1_1, 0.017863758 + keras_input.\"X_2\" * 0.12594759464263916 + keras_input.\"X_3\" * 0.6201684474945068 + keras_input.\"X_4\" * 0.48617687821388245 + keras_input.\"X_30\" * -0.1793716996908188 + keras_input.\"X_31\" * -0.1397959291934967 + keras_input.\"X_32\" * -0.02336321771144867 + keras_input.\"X_58\" * -0.8183432221412659 + keras_input.\"X_59\" * -0.23505675792694092 + keras_input.\"X_60\" * -0.31184646487236023 AS output_0_1_2, 0.017863758 + keras_input.\"X_3\" * 0.12594759464263916 + keras_input.\"X_4\" * 0.6201684474945068 + keras_input.\"X_5\" * 0.48617687821388245 + keras_input.\"X_31\" * -0.1793716996908188 + keras_input.\"X_32\" * -0.1397959291934967 + keras_input.\"X_33\" * -0.02336321771144867 + keras_input.\"X_59\" * -0.8183432221412659 + keras_input.\"X_60\" * -0.23505675792694092 + keras_input.\"X_61\" * -0.31184646487236023 AS output_0_1_3, 0.017863758 + keras_input.\"X_4\" * 0.12594759464263916 + keras_input.\"X_5\" * 0.6201684474945068 + keras_input.\"X_6\" * 0.48617687821388245 + keras_input.\"X_32\" * -0.1793716996908188 + keras_input.\"X_33\" * -0.1397959291934967 + keras_input.\"X_34\" * -0.02336321771144867 + keras_input.\"X_60\" * -0.8183432221412659 + keras_input.\"X_61\" 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keras_input.\"X_73\" * -0.1793716996908188 + keras_input.\"X_74\" * -0.1397959291934967 + keras_input.\"X_75\" * -0.02336321771144867 + keras_input.\"X_101\" * -0.8183432221412659 + keras_input.\"X_102\" * -0.23505675792694092 + keras_input.\"X_103\" * -0.31184646487236023 AS output_0_2_17, 0.017863758 + keras_input.\"X_46\" * 0.12594759464263916 + keras_input.\"X_47\" * 0.6201684474945068 + keras_input.\"X_48\" * 0.48617687821388245 + keras_input.\"X_74\" * -0.1793716996908188 + keras_input.\"X_75\" * -0.1397959291934967 + keras_input.\"X_76\" * -0.02336321771144867 + keras_input.\"X_102\" * -0.8183432221412659 + keras_input.\"X_103\" * -0.23505675792694092 + keras_input.\"X_104\" * -0.31184646487236023 AS output_0_2_18, 0.017863758 + keras_input.\"X_47\" * 0.12594759464263916 + keras_input.\"X_48\" * 0.6201684474945068 + keras_input.\"X_49\" * 0.48617687821388245 + keras_input.\"X_75\" * -0.1793716996908188 + keras_input.\"X_76\" * -0.1397959291934967 + keras_input.\"X_77\" * 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keras_input.\"X_114\" * -0.8183432221412659 + keras_input.\"X_115\" * -0.23505675792694092 + keras_input.\"X_116\" * -0.31184646487236023 AS output_0_3_2, 0.017863758 + keras_input.\"X_59\" * 0.12594759464263916 + keras_input.\"X_60\" * 0.6201684474945068 + keras_input.\"X_61\" * 0.48617687821388245 + keras_input.\"X_87\" * -0.1793716996908188 + keras_input.\"X_88\" * -0.1397959291934967 + keras_input.\"X_89\" * -0.02336321771144867 + keras_input.\"X_115\" * -0.8183432221412659 + keras_input.\"X_116\" * -0.23505675792694092 + keras_input.\"X_117\" * -0.31184646487236023 AS output_0_3_3, 0.017863758 + keras_input.\"X_60\" * 0.12594759464263916 + keras_input.\"X_61\" * 0.6201684474945068 + keras_input.\"X_62\" * 0.48617687821388245 + keras_input.\"X_88\" * -0.1793716996908188 + keras_input.\"X_89\" * -0.1397959291934967 + keras_input.\"X_90\" * -0.02336321771144867 + keras_input.\"X_116\" * -0.8183432221412659 + keras_input.\"X_117\" * -0.23505675792694092 + keras_input.\"X_118\" * 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0.6201684474945068 + keras_input.\"X_65\" * 0.48617687821388245 + keras_input.\"X_91\" * -0.1793716996908188 + keras_input.\"X_92\" * -0.1397959291934967 + keras_input.\"X_93\" * -0.02336321771144867 + keras_input.\"X_119\" * -0.8183432221412659 + keras_input.\"X_120\" * -0.23505675792694092 + keras_input.\"X_121\" * -0.31184646487236023 AS output_0_3_7, 0.017863758 + keras_input.\"X_64\" * 0.12594759464263916 + keras_input.\"X_65\" * 0.6201684474945068 + keras_input.\"X_66\" * 0.48617687821388245 + keras_input.\"X_92\" * -0.1793716996908188 + keras_input.\"X_93\" * -0.1397959291934967 + keras_input.\"X_94\" * -0.02336321771144867 + keras_input.\"X_120\" * -0.8183432221412659 + keras_input.\"X_121\" * -0.23505675792694092 + keras_input.\"X_122\" * -0.31184646487236023 AS output_0_3_8, 0.017863758 + keras_input.\"X_65\" * 0.12594759464263916 + keras_input.\"X_66\" * 0.6201684474945068 + keras_input.\"X_67\" * 0.48617687821388245 + keras_input.\"X_93\" * -0.1793716996908188 + keras_input.\"X_94\" * -0.1397959291934967 + keras_input.\"X_95\" * -0.02336321771144867 + keras_input.\"X_121\" * -0.8183432221412659 + keras_input.\"X_122\" * -0.23505675792694092 + keras_input.\"X_123\" * -0.31184646487236023 AS output_0_3_9, 0.017863758 + keras_input.\"X_66\" * 0.12594759464263916 + keras_input.\"X_67\" * 0.6201684474945068 + keras_input.\"X_68\" * 0.48617687821388245 + keras_input.\"X_94\" * -0.1793716996908188 + keras_input.\"X_95\" * -0.1397959291934967 + keras_input.\"X_96\" * -0.02336321771144867 + keras_input.\"X_122\" * -0.8183432221412659 + keras_input.\"X_123\" * -0.23505675792694092 + keras_input.\"X_124\" * -0.31184646487236023 AS output_0_3_10, 0.017863758 + keras_input.\"X_67\" * 0.12594759464263916 + keras_input.\"X_68\" * 0.6201684474945068 + keras_input.\"X_69\" * 0.48617687821388245 + keras_input.\"X_95\" * -0.1793716996908188 + keras_input.\"X_96\" * -0.1397959291934967 + keras_input.\"X_97\" * -0.02336321771144867 + keras_input.\"X_123\" * -0.8183432221412659 + keras_input.\"X_124\" * -0.23505675792694092 + keras_input.\"X_125\" * -0.31184646487236023 AS output_0_3_11, 0.017863758 + keras_input.\"X_68\" * 0.12594759464263916 + keras_input.\"X_69\" * 0.6201684474945068 + keras_input.\"X_70\" * 0.48617687821388245 + keras_input.\"X_96\" * -0.1793716996908188 + keras_input.\"X_97\" * -0.1397959291934967 + keras_input.\"X_98\" * -0.02336321771144867 + keras_input.\"X_124\" * -0.8183432221412659 + keras_input.\"X_125\" * -0.23505675792694092 + keras_input.\"X_126\" * -0.31184646487236023 AS output_0_3_12, 0.017863758 + keras_input.\"X_69\" * 0.12594759464263916 + keras_input.\"X_70\" * 0.6201684474945068 + keras_input.\"X_71\" * 0.48617687821388245 + keras_input.\"X_97\" * -0.1793716996908188 + keras_input.\"X_98\" * -0.1397959291934967 + keras_input.\"X_99\" * -0.02336321771144867 + keras_input.\"X_125\" * -0.8183432221412659 + keras_input.\"X_126\" * -0.23505675792694092 + keras_input.\"X_127\" * -0.31184646487236023 AS output_0_3_13, 0.017863758 + keras_input.\"X_70\" * 0.12594759464263916 + keras_input.\"X_71\" * 0.6201684474945068 + keras_input.\"X_72\" * 0.48617687821388245 + keras_input.\"X_98\" * -0.1793716996908188 + keras_input.\"X_99\" * -0.1397959291934967 + keras_input.\"X_100\" * -0.02336321771144867 + keras_input.\"X_126\" * -0.8183432221412659 + keras_input.\"X_127\" * -0.23505675792694092 + keras_input.\"X_128\" * -0.31184646487236023 AS output_0_3_14, 0.017863758 + keras_input.\"X_71\" * 0.12594759464263916 + keras_input.\"X_72\" * 0.6201684474945068 + keras_input.\"X_73\" * 0.48617687821388245 + keras_input.\"X_99\" * -0.1793716996908188 + keras_input.\"X_100\" * -0.1397959291934967 + keras_input.\"X_101\" * -0.02336321771144867 + keras_input.\"X_127\" * -0.8183432221412659 + keras_input.\"X_128\" * -0.23505675792694092 + keras_input.\"X_129\" * -0.31184646487236023 AS output_0_3_15, 0.017863758 + keras_input.\"X_72\" * 0.12594759464263916 + keras_input.\"X_73\" * 0.6201684474945068 + keras_input.\"X_74\" * 0.48617687821388245 + keras_input.\"X_100\" * -0.1793716996908188 + keras_input.\"X_101\" * -0.1397959291934967 + keras_input.\"X_102\" * -0.02336321771144867 + keras_input.\"X_128\" * -0.8183432221412659 + keras_input.\"X_129\" * -0.23505675792694092 + keras_input.\"X_130\" * -0.31184646487236023 AS output_0_3_16, 0.017863758 + keras_input.\"X_73\" * 0.12594759464263916 + keras_input.\"X_74\" * 0.6201684474945068 + keras_input.\"X_75\" * 0.48617687821388245 + keras_input.\"X_101\" * -0.1793716996908188 + keras_input.\"X_102\" * -0.1397959291934967 + keras_input.\"X_103\" * -0.02336321771144867 + keras_input.\"X_129\" * -0.8183432221412659 + keras_input.\"X_130\" * -0.23505675792694092 + keras_input.\"X_131\" * -0.31184646487236023 AS output_0_3_17, 0.017863758 + keras_input.\"X_74\" * 0.12594759464263916 + keras_input.\"X_75\" * 0.6201684474945068 + keras_input.\"X_76\" * 0.48617687821388245 + keras_input.\"X_102\" * -0.1793716996908188 + keras_input.\"X_103\" * -0.1397959291934967 + keras_input.\"X_104\" * -0.02336321771144867 + keras_input.\"X_130\" * -0.8183432221412659 + keras_input.\"X_131\" * -0.23505675792694092 + keras_input.\"X_132\" * -0.31184646487236023 AS output_0_3_18, 0.017863758 + keras_input.\"X_75\" * 0.12594759464263916 + keras_input.\"X_76\" * 0.6201684474945068 + keras_input.\"X_77\" * 0.48617687821388245 + keras_input.\"X_103\" * -0.1793716996908188 + keras_input.\"X_104\" * -0.1397959291934967 + keras_input.\"X_105\" * -0.02336321771144867 + keras_input.\"X_131\" * -0.818343222141\n" + ] + } + ], + "source": [ + "print(lSQL[0:50000])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Execute the SQL Code" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# save the dataset in a database table\n", + "\n", + "\n", + "import sqlalchemy as sa\n", + "\n", + "# engine = sa.create_engine('sqlite://' , echo=False)\n", + "engine = sa.create_engine(\"postgresql://db:db@localhost/db?port=5432\", echo=False)\n", + "conn = engine.connect()\n", + "NR = x_test.shape[0]\n", + "lTable = pd.DataFrame(x_test.reshape(NR , NC));\n", + "lTable.columns = lMetaData['features']\n", + "lTable['TGT'] = None\n", + "lTable['KEY'] = range(NR)\n", + "lTable.to_sql(lMetaData['table'] , conn, if_exists='replace', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "sql_output = pd.read_sql(lSQL , conn);\n", + "sql_output = sql_output.sort_values(by='KEY').reset_index(drop=True)\n", + "conn.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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