Commit 055b5e0e authored by Zhan Li's avatar Zhan Li

First upload by Group 4, Fabio, Helge, Mehdi, Robert, Zhan

parent 3e8a7394
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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"lucas_keras_fb.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"Cn_Ij_vWcnvi","colab_type":"code","outputId":"32ca3106-8a92-41dc-9377-7d706f27e8bf","executionInfo":{"status":"ok","timestamp":1583423115153,"user_tz":-60,"elapsed":2933,"user":{"displayName":"Zhan Li","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghe3dWPs_ch0ZobPWvmkvQ71iqhIu0LqLM5gOAczQ=s64","userId":"10084461076244130306"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["import os\n","import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","from sklearn import metrics\n","\n","%tensorflow_version 1.x\n","from keras.models import Sequential\n","from keras.layers import Dense, Conv1D, MaxPooling1D, Dropout, GlobalAveragePooling1D, Flatten, Reshape\n","from keras.utils import plot_model\n","\n","from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"},{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"i5IPORsocwKT","colab_type":"code","outputId":"4ca1fa8b-236a-411d-f82e-9d02e668adac","executionInfo":{"status":"ok","timestamp":1583423117053,"user_tz":-60,"elapsed":3189,"user":{"displayName":"Zhan Li","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghe3dWPs_ch0ZobPWvmkvQ71iqhIu0LqLM5gOAczQ=s64","userId":"10084461076244130306"}},"colab":{"base_uri":"https://localhost:8080/","height":244}},"source":["COLAB_DATA_FOLDER = \"/content/drive/My Drive/Colab Data/gfz-hackathon-2020\"\n","DATASET_REFLECTANCE = os.path.join(COLAB_DATA_FOLDER, \"LUCAS.csv\")\n","df_reflectance = pd.read_csv(DATASET_REFLECTANCE, low_memory=False)\n","df_reflectance = df_reflectance.drop(df_reflectance.columns[0], axis=1)\n","df_reflectance.head()"],"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>SOC</th>\n"," <th>clay</th>\n"," <th>CaCO3</th>\n"," <th>X500</th>\n"," <th>X502</th>\n"," <th>X504</th>\n"," <th>X506</th>\n"," <th>X508</th>\n"," <th>X510</th>\n"," <th>X512</th>\n"," <th>X514</th>\n"," <th>X516</th>\n"," <th>X518</th>\n"," <th>X520</th>\n"," <th>X522</th>\n"," <th>X524</th>\n"," <th>X526</th>\n"," <th>X528</th>\n"," <th>X530</th>\n"," <th>X532</th>\n"," <th>X534</th>\n"," <th>X536</th>\n"," <th>X538</th>\n"," <th>X540</th>\n"," <th>X542</th>\n"," <th>X544</th>\n"," <th>X546</th>\n"," <th>X548</th>\n"," <th>X550</th>\n"," <th>X552</th>\n"," <th>X554</th>\n"," <th>X556</th>\n"," <th>X558</th>\n"," <th>X560</th>\n"," <th>X562</th>\n"," <th>X564</th>\n"," <th>X566</th>\n"," <th>X568</th>\n"," <th>X570</th>\n"," <th>X572</th>\n"," <th>...</th>\n"," <th>X2420</th>\n"," <th>X2422</th>\n"," <th>X2424</th>\n"," <th>X2426</th>\n"," <th>X2428</th>\n"," <th>X2430</th>\n"," <th>X2432</th>\n"," <th>X2434</th>\n"," <th>X2436</th>\n"," <th>X2438</th>\n"," <th>X2440</th>\n"," <th>X2442</th>\n"," <th>X2444</th>\n"," <th>X2446</th>\n"," <th>X2448</th>\n"," <th>X2450</th>\n"," <th>X2452</th>\n"," <th>X2454</th>\n"," <th>X2456</th>\n"," <th>X2458</th>\n"," <th>X2460</th>\n"," <th>X2462</th>\n"," <th>X2464</th>\n"," <th>X2466</th>\n"," <th>X2468</th>\n"," <th>X2470</th>\n"," <th>X2472</th>\n"," <th>X2474</th>\n"," <th>X2476</th>\n"," <th>X2478</th>\n"," <th>X2480</th>\n"," <th>X2482</th>\n"," <th>X2484</th>\n"," <th>X2486</th>\n"," <th>X2488</th>\n"," <th>X2490</th>\n"," <th>X2492</th>\n"," <th>X2494</th>\n"," <th>X2496</th>\n"," <th>X2498</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>15.6</td>\n"," <td>40</td>\n"," <td>1</td>\n"," <td>0.137399</td>\n"," <td>0.139045</td>\n"," <td>0.140758</td>\n"," <td>0.142544</td>\n"," <td>0.144388</td>\n"," <td>0.146281</td>\n"," <td>0.148221</td>\n"," <td>0.150205</td>\n"," <td>0.152239</td>\n"," <td>0.154322</td>\n"," <td>0.156457</td>\n"," <td>0.158648</td>\n"," <td>0.160887</td>\n"," <td>0.163174</td>\n"," <td>0.165509</td>\n"," <td>0.167905</td>\n"," <td>0.170354</td>\n"," <td>0.172859</td>\n"," <td>0.175418</td>\n"," <td>0.178033</td>\n"," <td>0.180703</td>\n"," <td>0.183434</td>\n"," <td>0.186230</td>\n"," <td>0.189087</td>\n"," <td>0.192008</td>\n"," <td>0.194992</td>\n"," <td>0.198028</td>\n"," <td>0.201108</td>\n"," <td>0.204220</td>\n"," <td>0.207357</td>\n"," <td>0.210504</td>\n"," <td>0.213638</td>\n"," <td>0.216768</td>\n"," <td>0.219870</td>\n"," <td>0.222930</td>\n"," <td>0.225940</td>\n"," <td>0.228882</td>\n"," <td>...</td>\n"," <td>0.415256</td>\n"," <td>0.413955</td>\n"," <td>0.412602</td>\n"," <td>0.411199</td>\n"," <td>0.409773</td>\n"," <td>0.408343</td>\n"," <td>0.406926</td>\n"," <td>0.405535</td>\n"," <td>0.404165</td>\n"," <td>0.402817</td>\n"," <td>0.401511</td>\n"," <td>0.400255</td>\n"," <td>0.399061</td>\n"," <td>0.397940</td>\n"," <td>0.396882</td>\n"," <td>0.395856</td>\n"," <td>0.394848</td>\n"," <td>0.393832</td>\n"," <td>0.392825</td>\n"," <td>0.391824</td>\n"," <td>0.390808</td>\n"," <td>0.389789</td>\n"," <td>0.388794</td>\n"," <td>0.387802</td>\n"," <td>0.386802</td>\n"," <td>0.385828</td>\n"," <td>0.384866</td>\n"," <td>0.383890</td>\n"," <td>0.382878</td>\n"," <td>0.381844</td>\n"," <td>0.380845</td>\n"," <td>0.379886</td>\n"," <td>0.378946</td>\n"," <td>0.378047</td>\n"," <td>0.377186</td>\n"," <td>0.376361</td>\n"," <td>0.375612</td>\n"," <td>0.374935</td>\n"," <td>0.374315</td>\n"," <td>0.373759</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>12.8</td>\n"," <td>14</td>\n"," <td>0</td>\n"," <td>0.207886</td>\n"," <td>0.209963</td>\n"," <td>0.212104</td>\n"," <td>0.214308</td>\n"," <td>0.216571</td>\n"," <td>0.218889</td>\n"," <td>0.221238</td>\n"," <td>0.223627</td>\n"," <td>0.226044</td>\n"," <td>0.228486</td>\n"," <td>0.230945</td>\n"," <td>0.233419</td>\n"," <td>0.235900</td>\n"," <td>0.238385</td>\n"," <td>0.240868</td>\n"," <td>0.243360</td>\n"," <td>0.245846</td>\n"," <td>0.248327</td>\n"," <td>0.250804</td>\n"," <td>0.253284</td>\n"," <td>0.255765</td>\n"," <td>0.258253</td>\n"," <td>0.260755</td>\n"," <td>0.263272</td>\n"," <td>0.265810</td>\n"," <td>0.268360</td>\n"," <td>0.270923</td>\n"," <td>0.273504</td>\n"," <td>0.276099</td>\n"," <td>0.278707</td>\n"," <td>0.281322</td>\n"," <td>0.283926</td>\n"," <td>0.286536</td>\n"," <td>0.289125</td>\n"," <td>0.291688</td>\n"," <td>0.294253</td>\n"," <td>0.296793</td>\n"," <td>...</td>\n"," <td>0.506033</td>\n"," <td>0.504923</td>\n"," <td>0.503766</td>\n"," <td>0.502551</td>\n"," <td>0.501270</td>\n"," <td>0.499944</td>\n"," <td>0.498609</td>\n"," <td>0.497253</td>\n"," <td>0.495892</td>\n"," <td>0.494588</td>\n"," <td>0.493327</td>\n"," <td>0.492069</td>\n"," <td>0.490862</td>\n"," <td>0.489753</td>\n"," <td>0.488700</td>\n"," <td>0.487680</td>\n"," <td>0.486712</td>\n"," <td>0.485778</td>\n"," <td>0.484865</td>\n"," <td>0.483949</td>\n"," <td>0.483000</td>\n"," <td>0.482004</td>\n"," <td>0.480988</td>\n"," <td>0.480005</td>\n"," <td>0.479052</td>\n"," <td>0.478102</td>\n"," <td>0.477155</td>\n"," <td>0.476200</td>\n"," <td>0.475207</td>\n"," <td>0.474178</td>\n"," <td>0.473158</td>\n"," <td>0.472191</td>\n"," <td>0.471266</td>\n"," <td>0.470362</td>\n"," <td>0.469515</td>\n"," <td>0.468765</td>\n"," <td>0.468111</td>\n"," <td>0.467521</td>\n"," <td>0.466991</td>\n"," <td>0.466523</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>15.5</td>\n"," <td>32</td>\n"," <td>0</td>\n"," <td>0.141469</td>\n"," <td>0.143069</td>\n"," <td>0.144725</td>\n"," <td>0.146438</td>\n"," <td>0.148201</td>\n"," <td>0.150010</td>\n"," <td>0.151852</td>\n"," <td>0.153735</td>\n"," <td>0.155655</td>\n"," <td>0.157608</td>\n"," <td>0.159598</td>\n"," <td>0.161625</td>\n"," <td>0.163691</td>\n"," <td>0.165791</td>\n"," <td>0.167922</td>\n"," <td>0.170090</td>\n"," <td>0.172294</td>\n"," <td>0.174540</td>\n"," <td>0.176829</td>\n"," <td>0.179163</td>\n"," <td>0.181542</td>\n"," <td>0.183970</td>\n"," <td>0.186450</td>\n"," <td>0.188986</td>\n"," <td>0.191580</td>\n"," <td>0.194232</td>\n"," <td>0.196931</td>\n"," <td>0.199677</td>\n"," <td>0.202458</td>\n"," <td>0.205269</td>\n"," <td>0.208091</td>\n"," <td>0.210922</td>\n"," <td>0.213746</td>\n"," <td>0.216535</td>\n"," <td>0.219292</td>\n"," <td>0.222010</td>\n"," <td>0.224660</td>\n"," <td>...</td>\n"," <td>0.381245</td>\n"," <td>0.380459</td>\n"," <td>0.379669</td>\n"," <td>0.378855</td>\n"," <td>0.377993</td>\n"," <td>0.377084</td>\n"," <td>0.376166</td>\n"," <td>0.375261</td>\n"," <td>0.374373</td>\n"," <td>0.373521</td>\n"," <td>0.372695</td>\n"," <td>0.371898</td>\n"," <td>0.371139</td>\n"," <td>0.370423</td>\n"," <td>0.369762</td>\n"," <td>0.369165</td>\n"," <td>0.368613</td>\n"," <td>0.368063</td>\n"," <td>0.367500</td>\n"," <td>0.366940</td>\n"," <td>0.366381</td>\n"," <td>0.365803</td>\n"," <td>0.365229</td>\n"," <td>0.364689</td>\n"," <td>0.364174</td>\n"," <td>0.363649</td>\n"," <td>0.363105</td>\n"," <td>0.362559</td>\n"," <td>0.362004</td>\n"," <td>0.361426</td>\n"," <td>0.360846</td>\n"," <td>0.360263</td>\n"," <td>0.359673</td>\n"," <td>0.359103</td>\n"," <td>0.358567</td>\n"," <td>0.358092</td>\n"," <td>0.357702</td>\n"," <td>0.357367</td>\n"," <td>0.357054</td>\n"," <td>0.356789</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>24.3</td>\n"," <td>39</td>\n"," <td>34</td>\n"," <td>0.119935</td>\n"," <td>0.121761</td>\n"," <td>0.123656</td>\n"," <td>0.125622</td>\n"," <td>0.127645</td>\n"," <td>0.129723</td>\n"," <td>0.131852</td>\n"," <td>0.134032</td>\n"," <td>0.136266</td>\n"," <td>0.138548</td>\n"," <td>0.140879</td>\n"," <td>0.143262</td>\n"," <td>0.145699</td>\n"," <td>0.148188</td>\n"," <td>0.150725</td>\n"," <td>0.153315</td>\n"," <td>0.155953</td>\n"," <td>0.158637</td>\n"," <td>0.161368</td>\n"," <td>0.164146</td>\n"," <td>0.166963</td>\n"," <td>0.169824</td>\n"," <td>0.172725</td>\n"," <td>0.175656</td>\n"," <td>0.178620</td>\n"," <td>0.181611</td>\n"," <td>0.184617</td>\n"," <td>0.187631</td>\n"," <td>0.190641</td>\n"," <td>0.193636</td>\n"," <td>0.196602</td>\n"," <td>0.199522</td>\n"," <td>0.202403</td>\n"," <td>0.205217</td>\n"," <td>0.207958</td>\n"," <td>0.210639</td>\n"," <td>0.213241</td>\n"," <td>...</td>\n"," <td>0.393753</td>\n"," <td>0.392341</td>\n"," <td>0.390863</td>\n"," <td>0.389333</td>\n"," <td>0.387786</td>\n"," <td>0.386251</td>\n"," <td>0.384757</td>\n"," <td>0.383298</td>\n"," <td>0.381830</td>\n"," <td>0.380342</td>\n"," <td>0.378883</td>\n"," <td>0.377507</td>\n"," <td>0.376233</td>\n"," <td>0.375066</td>\n"," <td>0.374001</td>\n"," <td>0.372998</td>\n"," <td>0.371996</td>\n"," <td>0.370941</td>\n"," <td>0.369877</td>\n"," <td>0.368839</td>\n"," <td>0.367789</td>\n"," <td>0.366737</td>\n"," <td>0.365714</td>\n"," <td>0.364713</td>\n"," <td>0.363745</td>\n"," <td>0.362839</td>\n"," <td>0.361946</td>\n"," <td>0.360965</td>\n"," <td>0.359881</td>\n"," <td>0.358769</td>\n"," <td>0.357688</td>\n"," <td>0.356667</td>\n"," <td>0.355723</td>\n"," <td>0.354844</td>\n"," <td>0.354007</td>\n"," <td>0.353215</td>\n"," <td>0.352515</td>\n"," <td>0.351896</td>\n"," <td>0.351291</td>\n"," <td>0.350724</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>22.9</td>\n"," <td>37</td>\n"," <td>1</td>\n"," <td>0.124097</td>\n"," <td>0.125509</td>\n"," <td>0.126974</td>\n"," <td>0.128495</td>\n"," <td>0.130061</td>\n"," <td>0.131668</td>\n"," <td>0.133312</td>\n"," <td>0.134993</td>\n"," <td>0.136711</td>\n"," <td>0.138459</td>\n"," <td>0.140238</td>\n"," <td>0.142048</td>\n"," <td>0.143889</td>\n"," <td>0.145760</td>\n"," <td>0.147658</td>\n"," <td>0.149581</td>\n"," <td>0.151529</td>\n"," <td>0.153502</td>\n"," <td>0.155503</td>\n"," <td>0.157537</td>\n"," <td>0.159599</td>\n"," <td>0.161694</td>\n"," <td>0.163824</td>\n"," <td>0.165991</td>\n"," <td>0.168196</td>\n"," <td>0.170438</td>\n"," <td>0.172711</td>\n"," <td>0.175020</td>\n"," <td>0.177354</td>\n"," <td>0.179711</td>\n"," <td>0.182084</td>\n"," <td>0.184462</td>\n"," <td>0.186833</td>\n"," <td>0.189192</td>\n"," <td>0.191539</td>\n"," <td>0.193866</td>\n"," <td>0.196158</td>\n"," <td>...</td>\n"," <td>0.365272</td>\n"," <td>0.364104</td>\n"," <td>0.362872</td>\n"," <td>0.361600</td>\n"," <td>0.360320</td>\n"," <td>0.359048</td>\n"," <td>0.357822</td>\n"," <td>0.356637</td>\n"," <td>0.355432</td>\n"," <td>0.354208</td>\n"," <td>0.353005</td>\n"," <td>0.351843</td>\n"," <td>0.350766</td>\n"," <td>0.349800</td>\n"," <td>0.348907</td>\n"," <td>0.348046</td>\n"," <td>0.347183</td>\n"," <td>0.346294</td>\n"," <td>0.345390</td>\n"," <td>0.344484</td>\n"," <td>0.343584</td>\n"," <td>0.342721</td>\n"," <td>0.341886</td>\n"," <td>0.341066</td>\n"," <td>0.340269</td>\n"," <td>0.339534</td>\n"," <td>0.338829</td>\n"," <td>0.338053</td>\n"," <td>0.337177</td>\n"," <td>0.336275</td>\n"," <td>0.335420</td>\n"," <td>0.334649</td>\n"," <td>0.333944</td>\n"," <td>0.333287</td>\n"," <td>0.332655</td>\n"," <td>0.332050</td>\n"," <td>0.331488</td>\n"," <td>0.330957</td>\n"," <td>0.330449</td>\n"," <td>0.329996</td>\n"," </tr>\n"," </tbody>\n","</table>\n","<p>5 rows × 1003 columns</p>\n","</div>"],"text/plain":[" SOC clay CaCO3 X500 ... X2492 X2494 X2496 X2498\n","0 15.6 40 1 0.137399 ... 0.375612 0.374935 0.374315 0.373759\n","1 12.8 14 0 0.207886 ... 0.468111 0.467521 0.466991 0.466523\n","2 15.5 32 0 0.141469 ... 0.357702 0.357367 0.357054 0.356789\n","3 24.3 39 34 0.119935 ... 0.352515 0.351896 0.351291 0.350724\n","4 22.9 37 1 0.124097 ... 0.331488 0.330957 0.330449 0.329996\n","\n","[5 rows x 1003 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"code","metadata":{"id":"Y1saOHUld5OM","colab_type":"code","outputId":"564eea65-2b6f-4e72-fb19-1451725666a2","executionInfo":{"status":"ok","timestamp":1583423847263,"user_tz":-60,"elapsed":586,"user":{"displayName":"Zhan Li","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghe3dWPs_ch0ZobPWvmkvQ71iqhIu0LqLM5gOAczQ=s64","userId":"10084461076244130306"}},"colab":{"base_uri":"https://localhost:8080/","height":84}},"source":["df = df_reflectance\n","for i, col in enumerate(df.columns):\n"," if i >= 0 and i < 3:\n"," if df[col].dtype == np.int64 or df[col].dtype == np.float64:\n"," print(\"Column no.\", i, \"-\", col, \"- ranges from\", df[col].min(), \"to\", df[col].max())\n"," else:\n"," print(\"Column no.\", i, \"-\", col, \"- has possible values\", set(df[col]))\n","print(\"Multispectral properties range from\", df.iloc[:,3:].min().min(), \"to\", df.iloc[:,3:].max().max())\n"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Column no. 0 - SOC - ranges from 0.0 to 193.9\n","Column no. 1 - clay - ranges from 1 to 79\n","Column no. 2 - CaCO3 - ranges from 0 to 882\n","Multispectral properties range from 0.0426795665 to 0.8116503869\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"jRrKUlucaktQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"70af811b-8059-4395-9c26-1026a74f1e49","executionInfo":{"status":"ok","timestamp":1583423919475,"user_tz":-60,"elapsed":542,"user":{"displayName":"Zhan Li","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghe3dWPs_ch0ZobPWvmkvQ71iqhIu0LqLM5gOAczQ=s64","userId":"10084461076244130306"}}},"source":["df.shape"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(8327, 1003)"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"8RPxaVoAeNki","colab_type":"code","colab":{}},"source":["def get_dataframe_parts(df):\n"," df = df.sample(frac=1).reset_index(drop=True)\n"," \n"," total_num_rows = df.shape[0]\n"," training_set_size = int(sets_ratio[0] * total_num_rows)\n"," validation_set_size = int(sets_ratio[1] * total_num_rows)\n"," testing_set_size = total_num_rows - training_set_size - validation_set_size\n","\n"," df_training = df.iloc[:training_set_size, :]\n"," df_validation = df.iloc[training_set_size:training_set_size+validation_set_size]\n"," df_testing = df.tail(testing_set_size)\n","\n"," return df_training, df_validation, df_testing"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"em7D_KlHeYoB","colab_type":"code","colab":{}},"source":["# Select dataset type (\"absorbance\" or \"reflectance\")\n","dataset_type = \"reflectance\"\n","\n","# Training / validation / testing sets ratio (sum = 1)\n","sets_ratio = (0.75, 0.2, 0.05)\n"," \n","# Select labels column\n","labels_column_name = \"SOC\"\n","\n","df_training, df_validation, df_testing = get_dataframe_parts(df_reflectance if dataset_type == \"reflectance\" else df_absorbance)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"0RqQejD6eBLd","colab_type":"code","colab":{}},"source":["y_train = np.array(df_training[labels_column_name])\n","X_train = np.array(df_training.drop(['SOC', 'clay', 'CaCO3'], axis=1))\n","\n","y_validate = np.array(df_validation[labels_column_name])\n","X_validate = np.array(df_validation.drop(['SOC', 'clay', 'CaCO3'], axis=1))\n","\n","y_test = np.array(df_testing[labels_column_name])\n","X_test = np.array(df_testing.drop(['SOC', 'clay', 'CaCO3'], axis=1))"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"d81efcc6-37a7-4886-82ec-23651f864a07","executionInfo":{"status":"ok","timestamp":1583420153047,"user_tz":-60,"elapsed":1046,"user":{"displayName":"Zhan Li","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghe3dWPs_ch0ZobPWvmkvQ71iqhIu0LqLM5gOAczQ=s64","userId":"10084461076244130306"}},"id":"crv2WmaMMRzg","colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["from keras.models import Sequential\n","from keras.layers import Dense, Conv1D, MaxPooling1D, Dropout, GlobalAveragePooling1D, Flatten, Reshape\n","from keras.utils import plot_model\n","\n","regressor = Sequential()\n","\n","# add first input layer and hidden layer\n","#regressor.add(Dense(1000, kernel_initializer=\"uniform\", activation='relu', input_dim=1000))\n","\n","#regressor.add(Conv1D(256, 3, strides=1, padding='valid', \n","# data_format='channels_last', dilation_rate=1,\n","# activation=None, use_bias=True, \n","# kernel_initializer='glorot_uniform',\n","# bias_initializer='zeros', \n","# kernel_regularizer=None, \n","# bias_regularizer=None, \n","# activity_regularizer=None, \n","# kernel_constraint=None, bias_constraint=None))\n","#regressor.add(Flatten(data_format=None))\n","regressor.add(Reshape((1000,1), input_shape=(1000,)))\n","regressor.add(Conv1D(100, 10, activation='relu'))\n","regressor.add(Conv1D(100, 10, activation='relu'))\n","regressor.add(Conv1D(100, 10, activation='relu'))\n","regressor.add(MaxPooling1D(10))\n","regressor.add(Conv1D(100, 10, activation='relu'))\n","regressor.add(GlobalAveragePooling1D())\n","regressor.add(Dropout(0.5))\n","regressor.add(Dense(1))\n","# add second hidden layer\n","#regressor.add(Dense(500, kernel_initializer=\"uniform\", activation='relu'))\n","# add the output layer\n","#regressor.add(Dense(1, kernel_initializer=\"uniform\", activation='linear'))\n","#compile the ANN\n","regressor.compile(optimizer='adam', loss='mse', metrics=['mse', 'mae'])\n","#fitting the model\n","#history = regressor.fit(X_train,y_train, batch_size=10, nb_epoch=500, verbose=1, validation_split=0.2)\n","\n","plot_model(regressor, show_shapes=True)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4267: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n"],"name":"stdout"},{"output_type":"execute_result","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAArUAAAQtCAIAAADLJyACAAAABmJLR0QA/wD/AP+gvaeTAAAgAElE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NHLBY7ODhER0e3tbXp33vq1KkDBw50dnb23xHBIIL8AGDYGc73m+3evTstLW379u1BQUE3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\n3d1dWFioRz/BwcFdXV0p4T3LugAAHCZJREFUKSn6hcEhl8sfPXrEfJMe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object>"]},"metadata":{"tags":[]},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"-aHEzMx2evRx","colab_type":"code","outputId":"7e2108fd-a298-480f-e49c-034a345715af","executionInfo":{"status":"ok","timestamp":1583422171439,"user_tz":-60,"elapsed":2010024,"user":{"displayName":"Zhan Li","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghe3dWPs_ch0ZobPWvmkvQ71iqhIu0LqLM5gOAczQ=s64","userId":"10084461076244130306"}},"colab":{"base_uri":"https://localhost:8080/","height":675}},"source":["regressor.fit(X_train, y_train,\n"," batch_size=64,\n"," epochs=10,\n"," verbose=1,\n"," validation_data=(X_validate, y_validate))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:1020: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3005: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","Train on 6245 samples, validate on 1665 samples\n","Epoch 1/10\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","6245/6245 [==============================] - 202s 32ms/step - loss: 242.2688 - mean_squared_error: 242.2688 - mean_absolute_error: 9.6889 - val_loss: 196.7177 - val_mean_squared_error: 196.7177 - val_mean_absolute_error: 8.0377\n","Epoch 2/10\n","6245/6245 [==============================] - 201s 32ms/step - loss: 205.1800 - mean_squared_error: 205.1800 - mean_absolute_error: 8.5911 - val_loss: 220.6437 - val_mean_squared_error: 220.6437 - val_mean_absolute_error: 8.1467\n","Epoch 3/10\n","6245/6245 [==============================] - 202s 32ms/step - loss: 186.1628 - mean_squared_error: 186.1628 - mean_absolute_error: 8.1674 - val_loss: 172.7359 - val_mean_squared_error: 172.7359 - val_mean_absolute_error: 7.4291\n","Epoch 4/10\n","6245/6245 [==============================] - 201s 32ms/step - loss: 182.2241 - mean_squared_error: 182.2241 - mean_absolute_error: 8.0440 - val_loss: 176.5814 - val_mean_squared_error: 176.5814 - val_mean_absolute_error: 8.4098\n","Epoch 5/10\n","6245/6245 [==============================] - 201s 32ms/step - loss: 177.4690 - mean_squared_error: 177.4690 - mean_absolute_error: 7.9356 - val_loss: 168.7495 - val_mean_squared_error: 168.7495 - val_mean_absolute_error: 7.5510\n","Epoch 6/10\n","6245/6245 [==============================] - 201s 32ms/step - loss: 174.9179 - mean_squared_error: 174.9179 - mean_absolute_error: 7.9279 - val_loss: 166.7963 - val_mean_squared_error: 166.7963 - val_mean_absolute_error: 7.6024\n","Epoch 7/10\n","6245/6245 [==============================] - 201s 32ms/step - loss: 177.2078 - mean_squared_error: 177.2078 - mean_absolute_error: 8.0074 - val_loss: 170.4770 - val_mean_squared_error: 170.4770 - val_mean_absolute_error: 7.3287\n","Epoch 8/10\n","6245/6245 [==============================] - 200s 32ms/step - loss: 174.8005 - mean_squared_error: 174.8005 - mean_absolute_error: 7.9681 - val_loss: 171.1487 - val_mean_squared_error: 171.1487 - val_mean_absolute_error: 7.3469\n","Epoch 9/10\n","6245/6245 [==============================] - 201s 32ms/step - loss: 177.5167 - mean_squared_error: 177.5167 - mean_absolute_error: 7.9559 - val_loss: 166.1625 - val_mean_squared_error: 166.1625 - val_mean_absolute_error: 7.5394\n","Epoch 10/10\n","6245/6245 [==============================] - 200s 32ms/step - loss: 173.8715 - mean_squared_error: 173.8715 - mean_absolute_error: 7.9459 - val_loss: 165.2501 - val_mean_squared_error: 165.2501 - val_mean_absolute_error: 7.8986\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7f83c918fa90>"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"tCKpsuyYt_4R","colab_type":"code","colab":{}},"source":["y_pred = regressor.predict(X_test)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jVqnApwpuGDe","colab_type":"code","outputId":"3fe1c480-874b-4e71-b6fc-9adc3ecdcd85","executionInfo":{"status":"ok","timestamp":1583422207421,"user_tz":-60,"elapsed":645,"user":{"displayName":"Zhan Li","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghe3dWPs_ch0ZobPWvmkvQ71iqhIu0LqLM5gOAczQ=s64","userId":"10084461076244130306"}},"colab":{"base_uri":"https://localhost:8080/","height":50}},"source":["print (metrics.explained_variance_score(y_test, y_pred) )\n","print (metrics.r2_score(y_test, y_pred))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.16408120693430783\n","0.15014855499816204\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"tx_gYNYTuuUZ","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"tjnJ7m8nvv5r","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"DJ7BT6oNwnrc","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Le_yC94FxjEa","colab_type":"code","outputId":"a748d2fd-e7a8-480a-f00e-621a26477050","executionInfo":{"status":"ok","timestamp":1583418228499,"user_tz":-60,"elapsed":714,"user":{"displayName":"Fabio Brill","photoUrl":"","userId":"15503625559068180870"}},"colab":{"base_uri":"https://localhost:8080/","height":283}},"source":["idx = np.argsort(resultval[:,0])\n","plt.plot(resultval[idx, 0], 'r.', label='true')\n","plt.plot(resultval[idx, 1], 'b.', label='pred')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 0x7f68ad0bacc0>]"]},"metadata":{"tags":[]},"execution_count":153},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLrWUwOcAt0b14DngLnf/hpntBu4ws08DDwFbmphOEWmkWkv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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"Sr8M-KwrxlUi","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}
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