Datasets
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas\n",
"from sklearn.model_selection import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
" Sample Bacteria-1 Bacteria-2 Bacteria-3 Bacteria-4 Bacteria-5 \\\n",
"0 Sample-1 0.0 0.0 0.0 0.0 0.0 \n",
"1 Sample-2 0.0 0.0 0.0 0.0 0.0 \n",
"2 Sample-3 0.0 10.0 0.0 0.0 0.0 \n",
"3 Sample-4 0.0 17.0 0.0 0.0 0.0 \n",
"4 Sample-5 0.0 8.0 0.0 0.0 0.0 \n",
"\n",
" Bacteria-6 Bacteria-7 Bacteria-8 Bacteria-9 ... Bacteria-1086 \\\n",
"0 0.0 0.0 0.0 0.0 ... 0.0 \n",
"1 0.0 0.0 0.0 0.0 ... 0.0 \n",
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"\n",
" Bacteria-1092 Bacteria-1093 Bacteria-1094 disease \n",
"0 0.0 0.0 0.0 Disease-2 \n",
"1 0.0 0.0 0.0 Disease-2 \n",
"2 0.0 0.0 0.0 Disease-2 \n",
"3 0.0 0.0 0.0 Disease-2 \n",
"4 0.0 3.0 0.0 Disease-2 \n",
"\n",
"[5 rows x 1096 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pandas.read_csv('../data/challenge_1_gut_microbiome_data.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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" Bacteria-1 Bacteria-2 Bacteria-3 Bacteria-4 Bacteria-5 Bacteria-6 \\\n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 10.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 17.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 8.0 0.0 0.0 0.0 0.0 \n",
"\n",
" Bacteria-7 Bacteria-8 Bacteria-9 Bacteria-10 ... Bacteria-1085 \\\n",
"0 0.0 0.0 0.0 0.0 ... 0.0 \n",
"1 0.0 0.0 0.0 0.0 ... 0.0 \n",
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" Bacteria-1086 Bacteria-1087 Bacteria-1088 Bacteria-1089 Bacteria-1090 \\\n",
"0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 17111.0 0.0 0.0 0.0 \n",
"2 0.0 214.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 1932.0 0.0 0.0 0.0 \n",
"\n",
" Bacteria-1091 Bacteria-1092 Bacteria-1093 Bacteria-1094 \n",
"0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 3.0 0.0 \n",
"\n",
"[5 rows x 1094 columns]"
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},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# bacteria columns\n",
"bact = df[[i for i in df.columns if i != 'Sample' and i != 'disease']]\n",
"bact.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to normalize the data using 2 methods\n",
"1. mean normalization\n",
"2. min max normalization\n",
"\n",
"We will also have to find a way to take into account outliers. We can focus on this later."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# mean normalization\n",
"mean = bact.mean()\n",
"std = bact.std()\n",
"mean_normalized_df = (bact-mean) / std\n",
"\n",
"# min max normalization\n",
"min = bact.min()\n",
"max = bact.max()\n",
"min_max_normalized_df = (bact-min) / (max-min)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
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" <td>-0.012043</td>\n",
" <td>-0.073048</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.034875</td>\n",
" <td>-0.02676</td>\n",
" <td>-0.017774</td>\n",
" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>0.452891</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.040493</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 1094 columns</p>\n",
"</div>"
],
"text/plain": [
" Bacteria-1 Bacteria-2 Bacteria-3 Bacteria-4 Bacteria-5 Bacteria-6 \\\n",
"0 -0.012043 -0.092816 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"1 -0.012043 -0.092816 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"2 -0.012043 -0.068107 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"3 -0.012043 -0.050810 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"4 -0.012043 -0.073048 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"\n",
" Bacteria-7 Bacteria-8 Bacteria-9 Bacteria-10 ... Bacteria-1085 \\\n",
"0 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"1 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"2 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"3 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"4 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"\n",
" Bacteria-1086 Bacteria-1087 Bacteria-1088 Bacteria-1089 Bacteria-1090 \\\n",
"0 -0.011562 -0.217529 -0.011562 -0.011562 -0.011562 \n",
"1 -0.011562 5.720131 -0.011562 -0.011562 -0.011562 \n",
"2 -0.011562 -0.143269 -0.011562 -0.011562 -0.011562 \n",
"3 -0.011562 -0.217529 -0.011562 -0.011562 -0.011562 \n",
"4 -0.011562 0.452891 -0.011562 -0.011562 -0.011562 \n",
"\n",
" Bacteria-1091 Bacteria-1092 Bacteria-1093 Bacteria-1094 \n",
"0 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"1 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"2 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"3 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"4 -0.013835 -0.011562 -0.040493 -0.029902 \n",
"\n",
"[5 rows x 1094 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_normalized_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Bacteria-1</th>\n",
" <th>Bacteria-2</th>\n",
" <th>Bacteria-3</th>\n",
" <th>Bacteria-4</th>\n",
" <th>Bacteria-5</th>\n",
" <th>Bacteria-6</th>\n",
" <th>Bacteria-7</th>\n",
" <th>Bacteria-8</th>\n",
" <th>Bacteria-9</th>\n",
" <th>Bacteria-10</th>\n",
" <th>...</th>\n",
" <th>Bacteria-1085</th>\n",
" <th>Bacteria-1086</th>\n",
" <th>Bacteria-1087</th>\n",
" <th>Bacteria-1088</th>\n",
" <th>Bacteria-1089</th>\n",
" <th>Bacteria-1090</th>\n",
" <th>Bacteria-1091</th>\n",
" <th>Bacteria-1092</th>\n",
" <th>Bacteria-1093</th>\n",
" <th>Bacteria-1094</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
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" <td>0.214161</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0</td>\n",
" <td>0.000755</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>0.001283</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
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" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.000604</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 1094 columns</p>\n",
"</div>"
],
"text/plain": [
" Bacteria-1 Bacteria-2 Bacteria-3 Bacteria-4 Bacteria-5 Bacteria-6 \\\n",
"0 0.0 0.000000 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.000000 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.000755 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.001283 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.000604 0.0 0.0 0.0 0.0 \n",
"\n",
" Bacteria-7 Bacteria-8 Bacteria-9 Bacteria-10 ... Bacteria-1085 \\\n",
"0 0.0 0.0 0.0 0.0 ... 0.0 \n",
"1 0.0 0.0 0.0 0.0 ... 0.0 \n",
"2 0.0 0.0 0.0 0.0 ... 0.0 \n",
"3 0.0 0.0 0.0 0.0 ... 0.0 \n",
"4 0.0 0.0 0.0 0.0 ... 0.0 \n",
"\n",
" Bacteria-1086 Bacteria-1087 Bacteria-1088 Bacteria-1089 Bacteria-1090 \\\n",
"0 0.0 0.000000 0.0 0.0 0.0 \n",
"1 0.0 0.214161 0.0 0.0 0.0 \n",
"2 0.0 0.002678 0.0 0.0 0.0 \n",
"3 0.0 0.000000 0.0 0.0 0.0 \n",
"4 0.0 0.024181 0.0 0.0 0.0 \n",
"\n",
" Bacteria-1091 Bacteria-1092 Bacteria-1093 Bacteria-1094 \n",
"0 0.0 0.0 0.000000 0.0 \n",
"1 0.0 0.0 0.000000 0.0 \n",
"2 0.0 0.0 0.000000 0.0 \n",
"3 0.0 0.0 0.000000 0.0 \n",
"4 0.0 0.0 0.000056 0.0 \n",
"\n",
"[5 rows x 1094 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"min_max_normalized_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Bacteria-1', 'Bacteria-2', 'Bacteria-3', 'Bacteria-4', 'Bacteria-5',\n",
" 'Bacteria-6', 'Bacteria-7', 'Bacteria-8', 'Bacteria-9', 'Bacteria-10',\n",
" ...\n",
" 'Bacteria-1085', 'Bacteria-1086', 'Bacteria-1087', 'Bacteria-1088',\n",
" 'Bacteria-1089', 'Bacteria-1090', 'Bacteria-1091', 'Bacteria-1092',\n",
" 'Bacteria-1093', 'Bacteria-1094'],\n",
" dtype='object', length=1094)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mndf_cols = mean_normalized_df.columns\n",
"mndf_cols"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bacteria-1\n",
"+ 86.40301892406224\n",
"+ 3.6651938548249228\n",
"Bacteria-2\n",
"+ 9.996072300728644\n",
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"+ 27.134580609776723\n",
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"+ 5.14065580313933\n",
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"+ 4.303003796684991\n",
"+ 10.245638237754862\n",
"+ 4.26593954861179\n",
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"+ 4.745303823691855\n",
"+ 11.69608581235279\n",
"+ 23.907520077536702\n",
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"Bacteria-3\n",
"+ 8.742096857140032\n",
"+ 3.2203452032845292\n",
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"+ 3.2203452032845292\n",
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"+ 69.98662337081839\n",
"+ 3.81582822477875\n",
"+ 3.0218508627864553\n",
"Bacteria-4\n",
"+ 86.48121260523978\n",
"Bacteria-5\n",
"+ 86.48121260525441\n",
"Bacteria-6\n",
"+ 86.4812126052467\n",
"Bacteria-7\n",
"+ 3.0336498359463078\n",
"+ 5.181617358267574\n",
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"+ 4.030920471309753\n",
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"+ 20.370816266110815\n",
"+ 64.55757672529116\n",
"Bacteria-8\n",
"+ 23.27908592544745\n",
"+ 40.011488097648865\n",
"+ 21.48632854985444\n",
"+ 7.144269545110366\n",
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"Bacteria-9\n",
"+ 9.831898552448779\n",
"+ 12.294316800552876\n",
"+ 31.993662785385634\n",
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"Bacteria-10\n",
"+ 29.280059079020845\n",
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"+ 22.43654949070431\n",
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"+ 13.63775144858305\n",
"Bacteria-11\n",
"+ 86.48121260523962\n",
"Bacteria-12\n",
"+ 86.48121260524091\n",
"Bacteria-13\n",
"+ 86.48121260524269\n",
"Bacteria-14\n",
"+ 86.48121260523956\n",
"Bacteria-15\n",
"+ 86.48121260525394\n",
"Bacteria-16\n",
"+ 86.48121260524215\n",
"Bacteria-17\n",
"+ 8.737182963864607\n",
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"Bacteria-18\n",
"+ 86.48121260524027\n",
"Bacteria-19\n",
"+ 38.47594324045513\n",
"+ 73.47065843058363\n",
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"Bacteria-20\n",
"+ 22.756593421461012\n",
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"+ 18.198613339553226\n",
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"Bacteria-21\n",
"+ 6.150057430456359\n",
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"Bacteria-22\n",
"+ 38.5954006727526\n",
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"Bacteria-23\n",
"+ 31.463928702795304\n",
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"Bacteria-24\n",
"+ 8.871304590481307\n",
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"Bacteria-25\n",
"+ 86.48121260524293\n",
"Bacteria-26\n",
"+ 40.08812156479933\n",
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"Bacteria-27\n",
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"Bacteria-28\n",
"+ 86.48121260524054\n",
"Bacteria-29\n",
"+ 3.245405810126068\n",
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"Bacteria-30\n",
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"Bacteria-31\n",
"+ 47.96448536386405\n",
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"Bacteria-32\n",
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"Bacteria-33\n",
"+ 79.02675633853993\n",
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"Bacteria-34\n",
"+ 61.14736407787352\n",
"+ 61.14736407787352\n",
"Bacteria-35\n",
"+ 12.22099071539219\n",
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"Bacteria-36\n",
"+ 86.48121260523789\n",
"Bacteria-37\n",
"+ 86.48121260523949\n",
"Bacteria-38\n",
"+ 47.96448536386902\n",
"+ 71.95474510894518\n",
"Bacteria-39\n",
"+ 86.48121260523475\n",
"Bacteria-40\n",
"+ 35.585766495676175\n",
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"Bacteria-41\n",
"+ 8.820629301720816\n",
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"+ 26.06334024957767\n",
"+ 26.06334024957767\n",
"Bacteria-1084\n",
"+ 86.48121260524051\n",
"Bacteria-1085\n",
"+ 86.4812126052357\n",
"Bacteria-1086\n",
"+ 86.48121260523135\n",
"Bacteria-1087\n",
"+ 5.7201305419257915\n",
"+ 4.134302446844649\n",
"+ 4.6732063925035146\n",
"+ 3.880639353298847\n",
"+ 6.237172962422706\n",
"+ 6.414147213733731\n",
"+ 27.507742928328348\n",
"+ 7.224064669733597\n",
"+ 4.4722885660151155\n",
"+ 3.330631140891055\n",
"+ 3.3247319991806874\n",
"+ 3.021446713600618\n",
"+ 9.614952104111948\n",
"+ 3.436121675005862\n",
"+ 3.561391684267195\n",
"+ 3.0155475718902505\n",
"+ 4.437240724088815\n",
"+ 3.757451394052938\n",
"+ 7.659907139628983\n",
"+ 4.730115759591765\n",
"+ 21.22238094009974\n",
"+ 22.325867448274362\n",
"+ 6.453706164026784\n",
"+ 6.7760769080815715\n",
"+ 6.132723453315611\n",
"+ 3.192174814865371\n",
"+ 3.4902549754068812\n",
"+ 3.1425526228311034\n",
"+ 5.882877451464753\n",
"+ 4.0034803042088525\n",
"+ 3.5641677509544265\n",
"+ 6.500205281037915\n",
"+ 3.0367150803803926\n",
"+ 5.240218013370601\n",
"+ 4.974756636404064\n",
"+ 4.175596438817221\n",
"+ 7.788647232249355\n",
"+ 11.732049961462069\n",
"+ 3.2334688068379434\n",
"+ 3.356309757747949\n",
"+ 10.8256641880809\n",
"+ 4.012155512606451\n",
"+ 4.1717793471222775\n",
"+ 7.8007925240059945\n",
"+ 7.809120724067689\n",
"+ 5.7520553088289565\n",
"+ 11.66230128594537\n",
"+ 5.194759921367181\n",
"+ 8.681152672194365\n",
"+ 8.399381903440343\n",
"+ 4.530932974782886\n",
"+ 3.9590632372131442\n",
"+ 4.76724565153349\n",
"+ 9.865145114298711\n",
"+ 4.835606293706572\n",
"+ 4.287680131314204\n",
"+ 14.447390189910632\n",
"+ 4.222789572500162\n",
"+ 5.744074117103165\n",
"+ 13.198507188992245\n",
"+ 4.788066151687728\n",
"+ 7.997199242127642\n",
"+ 4.377555290313332\n",
"+ 9.747509288427265\n",
"+ 3.639815568181492\n",
"+ 3.677639476795025\n",
"+ 5.495269140260019\n",
"+ 4.229729739218241\n",
"+ 3.77861890254308\n",
"+ 3.6502258182586114\n",
"+ 7.167155302645345\n",
"+ 4.339731381699799\n",
"+ 4.0943964882156925\n",
"+ 3.7407949939295473\n",
"+ 3.6245472014017173\n",
"+ 3.01901765524929\n",
"+ 3.2070961733092416\n",
"+ 7.089078427066952\n",
"+ 5.081635203862486\n",
"+ 7.197692036204895\n",
"+ 7.744924181925455\n",
"+ 11.01894783117941\n",
"+ 12.87648345327336\n",
"+ 10.96897863080924\n",
"+ 3.4642293502140835\n",
"+ 7.920857408228768\n",
"+ 5.233277846652522\n",
"+ 4.198498988986883\n",
"+ 3.7636975440992093\n",
"+ 5.136809529271218\n",
"+ 8.625978346785635\n",
"+ 5.390819631152924\n",
"+ 5.0458933452643775\n",
"+ 3.4413268000444215\n",
"+ 3.257412382015317\n",
"+ 10.78298216276471\n",
"+ 7.285832153524503\n",
"+ 3.4406327833726134\n",
"+ 3.5870703011240885\n",
"+ 8.719323589143803\n",
"+ 3.5929694428344563\n",
"+ 5.385961514450268\n",
"+ 22.158956438704553\n",
"+ 13.437248924094176\n",
"+ 3.857736803129185\n",
"+ 3.4652703752217953\n",
"+ 3.386499482971594\n",
"+ 3.765432585778729\n",
"+ 4.86510200225841\n",
"Bacteria-1088\n",
"+ 86.48121260523183\n",
"Bacteria-1089\n",
"+ 86.48121260524944\n",
"Bacteria-1090\n",
"+ 86.4812126052386\n",
"Bacteria-1091\n",
"+ 12.50007987832646\n",
"+ 85.49791734715859\n",
"+ 3.114643632333756\n",
"Bacteria-1092\n",
"+ 86.4812126052403\n",
"Bacteria-1093\n",
"+ 24.154546330387348\n",
"+ 62.59152059644451\n",
"+ 3.326732160671814\n",
"+ 3.5820887766625304\n",
"+ 4.4270870332136285\n",
"+ 10.627609954224571\n",
"+ 36.90496645156017\n",
"+ 6.313243855872331\n",
"+ 19.581341480371787\n",
"+ 16.823490027672047\n",
"+ 4.950568095994598\n",
"+ 24.221867620057626\n",
"+ 3.3952141622329606\n",
"+ 4.892532501451253\n",
"+ 3.6436065068784758\n",
"Bacteria-1094\n",
"+ 12.415259233490417\n",
"+ 29.00880699695534\n",
"+ 3.0547963925614847\n",
"+ 6.458601061990188\n",
"+ 18.637839644789764\n",
"+ 4.065300903798131\n",
"+ 11.192016930414477\n",
"+ 66.76976504843002\n",
"+ 18.797392988669234\n",
"+ 6.086309926271423\n",
"+ 6.777707749749129\n",
"+ 32.14668942658493\n"
]
}
],
"source": [
"# removes the crazy outliers\n",
"count = 0\n",
"\n",
"for i, col in enumerate(mndf_cols):\n",
" print(col)\n",
" rows = mean_normalized_df[col]\n",
" for j, row in enumerate(rows):\n",
" if row < -5:\n",
" mean_normalized_df.at[j, col] = -5\n",
" print('-', row)\n",
" elif row > 5:\n",
" print('+', row)\n",
" mean_normalized_df.at[j, col] = 5"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# bact.at[946, 'Bacteria-1087']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"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>Bacteria-1</th>\n",
" <th>Bacteria-2</th>\n",
" <th>Bacteria-3</th>\n",
" <th>Bacteria-4</th>\n",
" <th>Bacteria-5</th>\n",
" <th>Bacteria-6</th>\n",
" <th>Bacteria-7</th>\n",
" <th>Bacteria-8</th>\n",
" <th>Bacteria-9</th>\n",
" <th>Bacteria-10</th>\n",
" <th>...</th>\n",
" <th>Bacteria-1085</th>\n",
" <th>Bacteria-1086</th>\n",
" <th>Bacteria-1087</th>\n",
" <th>Bacteria-1088</th>\n",
" <th>Bacteria-1089</th>\n",
" <th>Bacteria-1090</th>\n",
" <th>Bacteria-1091</th>\n",
" <th>Bacteria-1092</th>\n",
" <th>Bacteria-1093</th>\n",
" <th>Bacteria-1094</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.012043</td>\n",
" <td>-0.092816</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
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" <td>-0.011562</td>\n",
" <td>-0.034875</td>\n",
" <td>-0.02676</td>\n",
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" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.217529</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.043975</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.012043</td>\n",
" <td>-0.092816</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.034875</td>\n",
" <td>-0.02676</td>\n",
" <td>-0.017774</td>\n",
" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>5.000000</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.043975</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.012043</td>\n",
" <td>-0.068107</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.034875</td>\n",
" <td>-0.02676</td>\n",
" <td>-0.017774</td>\n",
" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.143269</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.043975</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.012043</td>\n",
" <td>-0.050810</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.034875</td>\n",
" <td>-0.02676</td>\n",
" <td>-0.017774</td>\n",
" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.217529</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.043975</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.012043</td>\n",
" <td>-0.073048</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.034875</td>\n",
" <td>-0.02676</td>\n",
" <td>-0.017774</td>\n",
" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>0.452891</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.040493</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 1094 columns</p>\n",
"</div>"
],
"text/plain": [
" Bacteria-1 Bacteria-2 Bacteria-3 Bacteria-4 Bacteria-5 Bacteria-6 \\\n",
"0 -0.012043 -0.092816 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"1 -0.012043 -0.092816 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"2 -0.012043 -0.068107 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"3 -0.012043 -0.050810 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"4 -0.012043 -0.073048 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"\n",
" Bacteria-7 Bacteria-8 Bacteria-9 Bacteria-10 ... Bacteria-1085 \\\n",
"0 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"1 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"2 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"3 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"4 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"\n",
" Bacteria-1086 Bacteria-1087 Bacteria-1088 Bacteria-1089 Bacteria-1090 \\\n",
"0 -0.011562 -0.217529 -0.011562 -0.011562 -0.011562 \n",
"1 -0.011562 5.000000 -0.011562 -0.011562 -0.011562 \n",
"2 -0.011562 -0.143269 -0.011562 -0.011562 -0.011562 \n",
"3 -0.011562 -0.217529 -0.011562 -0.011562 -0.011562 \n",
"4 -0.011562 0.452891 -0.011562 -0.011562 -0.011562 \n",
"\n",
" Bacteria-1091 Bacteria-1092 Bacteria-1093 Bacteria-1094 \n",
"0 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"1 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"2 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"3 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"4 -0.013835 -0.011562 -0.040493 -0.029902 \n",
"\n",
"[5 rows x 1094 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_normalized_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"# mean normalization\n",
"mean = bact.mean()\n",
"std = bact.std()\n",
"mean_normalized_df = (bact-mean) / std\n",
"\n",
"# min max normalization\n",
"min = bact.min()\n",
"max = bact.max()\n",
"min_max_normalized_df = (bact-min) / (max-min)\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: middle;\n",
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" 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>Bacteria-1</th>\n",
" <th>Bacteria-2</th>\n",
" <th>Bacteria-3</th>\n",
" <th>Bacteria-4</th>\n",
" <th>Bacteria-5</th>\n",
" <th>Bacteria-6</th>\n",
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" <th>Bacteria-8</th>\n",
" <th>Bacteria-9</th>\n",
" <th>Bacteria-10</th>\n",
" <th>...</th>\n",
" <th>Bacteria-1085</th>\n",
" <th>Bacteria-1086</th>\n",
" <th>Bacteria-1087</th>\n",
" <th>Bacteria-1088</th>\n",
" <th>Bacteria-1089</th>\n",
" <th>Bacteria-1090</th>\n",
" <th>Bacteria-1091</th>\n",
" <th>Bacteria-1092</th>\n",
" <th>Bacteria-1093</th>\n",
" <th>Bacteria-1094</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.012043</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.012043</td>\n",
" <td>-0.092816</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.034875</td>\n",
" <td>-0.02676</td>\n",
" <td>-0.017774</td>\n",
" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>5.720133</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.043975</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.012043</td>\n",
" <td>-0.068107</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
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" <td>-0.02676</td>\n",
" <td>-0.017774</td>\n",
" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.143251</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.043975</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.012043</td>\n",
" <td>-0.050810</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.034875</td>\n",
" <td>-0.02676</td>\n",
" <td>-0.017774</td>\n",
" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.217510</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.043975</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-0.012043</td>\n",
" <td>-0.073048</td>\n",
" <td>-0.045789</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.034875</td>\n",
" <td>-0.02676</td>\n",
" <td>-0.017774</td>\n",
" <td>-0.049268</td>\n",
" <td>...</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>0.452908</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.013835</td>\n",
" <td>-0.011562</td>\n",
" <td>-0.040493</td>\n",
" <td>-0.029902</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 1094 columns</p>\n",
"</div>"
],
"text/plain": [
" Bacteria-1 Bacteria-2 Bacteria-3 Bacteria-4 Bacteria-5 Bacteria-6 \\\n",
"0 -0.012043 -0.092816 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"1 -0.012043 -0.092816 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"2 -0.012043 -0.068107 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"3 -0.012043 -0.050810 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"4 -0.012043 -0.073048 -0.045789 -0.011562 -0.011562 -0.011562 \n",
"\n",
" Bacteria-7 Bacteria-8 Bacteria-9 Bacteria-10 ... Bacteria-1085 \\\n",
"0 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"1 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"2 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"3 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"4 -0.034875 -0.02676 -0.017774 -0.049268 ... -0.011562 \n",
"\n",
" Bacteria-1086 Bacteria-1087 Bacteria-1088 Bacteria-1089 Bacteria-1090 \\\n",
"0 -0.011562 -0.217510 -0.011562 -0.011562 -0.011562 \n",
"1 -0.011562 5.720133 -0.011562 -0.011562 -0.011562 \n",
"2 -0.011562 -0.143251 -0.011562 -0.011562 -0.011562 \n",
"3 -0.011562 -0.217510 -0.011562 -0.011562 -0.011562 \n",
"4 -0.011562 0.452908 -0.011562 -0.011562 -0.011562 \n",
"\n",
" Bacteria-1091 Bacteria-1092 Bacteria-1093 Bacteria-1094 \n",
"0 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"1 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"2 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"3 -0.013835 -0.011562 -0.043975 -0.029902 \n",
"4 -0.013835 -0.011562 -0.040493 -0.029902 \n",
"\n",
"[5 rows x 1094 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# mean_normalized_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" }\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>Bacteria-1</th>\n",
" <th>Bacteria-2</th>\n",
" <th>Bacteria-3</th>\n",
" <th>Bacteria-4</th>\n",
" <th>Bacteria-5</th>\n",
" <th>Bacteria-6</th>\n",
" <th>Bacteria-7</th>\n",
" <th>Bacteria-8</th>\n",
" <th>Bacteria-9</th>\n",
" <th>Bacteria-10</th>\n",
" <th>...</th>\n",
" <th>Bacteria-1085</th>\n",
" <th>Bacteria-1086</th>\n",
" <th>Bacteria-1087</th>\n",
" <th>Bacteria-1088</th>\n",
" <th>Bacteria-1089</th>\n",
" <th>Bacteria-1090</th>\n",
" <th>Bacteria-1091</th>\n",
" <th>Bacteria-1092</th>\n",
" <th>Bacteria-1093</th>\n",
" <th>Bacteria-1094</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <td>0.0</td>\n",
" <td>0.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.214161</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <th>2</th>\n",
" <td>0.0</td>\n",
" <td>0.000755</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" <td>0.001283</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.000604</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.024181</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000056</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 1094 columns</p>\n",
"</div>"
],
"text/plain": [
" Bacteria-1 Bacteria-2 Bacteria-3 Bacteria-4 Bacteria-5 Bacteria-6 \\\n",
"0 0.0 0.000000 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.000000 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.000755 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.001283 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.000604 0.0 0.0 0.0 0.0 \n",
"\n",
" Bacteria-7 Bacteria-8 Bacteria-9 Bacteria-10 ... Bacteria-1085 \\\n",
"0 0.0 0.0 0.0 0.0 ... 0.0 \n",
"1 0.0 0.0 0.0 0.0 ... 0.0 \n",
"2 0.0 0.0 0.0 0.0 ... 0.0 \n",
"3 0.0 0.0 0.0 0.0 ... 0.0 \n",
"4 0.0 0.0 0.0 0.0 ... 0.0 \n",
"\n",
" Bacteria-1086 Bacteria-1087 Bacteria-1088 Bacteria-1089 Bacteria-1090 \\\n",
"0 0.0 0.000000 0.0 0.0 0.0 \n",
"1 0.0 0.214161 0.0 0.0 0.0 \n",
"2 0.0 0.002678 0.0 0.0 0.0 \n",
"3 0.0 0.000000 0.0 0.0 0.0 \n",
"4 0.0 0.024181 0.0 0.0 0.0 \n",
"\n",
" Bacteria-1091 Bacteria-1092 Bacteria-1093 Bacteria-1094 \n",
"0 0.0 0.0 0.000000 0.0 \n",
"1 0.0 0.0 0.000000 0.0 \n",
"2 0.0 0.0 0.000000 0.0 \n",
"3 0.0 0.0 0.000000 0.0 \n",
"4 0.0 0.0 0.000056 0.0 \n",
"\n",
"[5 rows x 1094 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# min_max_normalized_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 Disease-2\n",
"1 Disease-2\n",
"2 Disease-2\n",
"3 Disease-2\n",
"4 Disease-2\n",
"Name: disease, dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels = df['disease']\n",
"labels.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-11-c77afe4adc21>:15: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" labels[i] = 2\n",
"<ipython-input-11-c77afe4adc21>:11: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" labels[i] = 0\n",
"<ipython-input-11-c77afe4adc21>:13: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" labels[i] = 1\n",
"<ipython-input-11-c77afe4adc21>:17: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" labels[i] = 3\n"
]
},
{
"data": {
"text/plain": [
"0 2\n",
"1 2\n",
"2 2\n",
"3 2\n",
"4 2\n",
"Name: disease, dtype: object"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# we are going to categorize the labels\n",
"# there is an incorrect matching in the csv file\n",
"# in csv Disease-1 maps to Disease-0 in the info form\n",
"# in csv Disease-3 maps to Disease-1 in the info form\n",
"# in csv Disease-2 maps to Disease-2 in the info form\n",
"# in csv Healthy maps to Healthy in the info form\n",
"possible_labels = ['Disease-1', 'Disease-3', 'Disease-2', 'Healthy']\n",
"\n",
"for i, label in enumerate(labels):\n",
" if label == possible_labels[0]:\n",
" labels[i] = 0\n",
" elif label == possible_labels[1]:\n",
" labels[i] = 1\n",
" elif label == possible_labels[2]:\n",
" labels[i] = 2\n",
" elif label == possible_labels[3]:\n",
" labels[i] = 3\n",
"\n",
"labels.head()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nfor label in labels:\\n if type(label) != int:\\n print(type(label))\\n'"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# make sure everything is properly categorized\n",
"'''\n",
"for label in labels:\n",
" if type(label) != int:\n",
" print(type(label))\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(7481, 1094)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_normalized_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"mean_normalized_df = np.asarray(mean_normalized_df).astype('float32')\n",
"labels = np.asarray(labels).astype('float32')\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" mean_normalized_df, \n",
" labels, \n",
" test_size=0.2, \n",
" random_state=3\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5984, 1094)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1497, 1094)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5984, 4)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from tensorflow.keras.utils import to_categorical\n",
"\n",
"y_train_categorical = to_categorical(y_train, num_classes=4)\n",
"y_train_categorical.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1497, 4)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_test_categorical = to_categorical(y_test, num_classes=4)\n",
"y_test_categorical.shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.95959466 1.54183842 3.23572664 0.49993317]\n",
"6.237092897624814\n",
"[0.15385287 0.24720466 0.51878763 0.08015484]\n"
]
}
],
"source": [
"# compute class weights\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"\n",
"class_weight = compute_class_weight(\n",
" class_weight='balanced',\n",
" classes = [0,1,2,3], \n",
" y=labels\n",
")\n",
"print(class_weight)\n",
"\n",
"sum = 0\n",
"for i in class_weight:\n",
" sum += i\n",
"print(sum)\n",
"\n",
"class_weight = class_weight / sum\n",
"print(class_weight)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras import Sequential\n",
"from tensorflow.keras.layers import Dense, Input, Concatenate, Maximum, Dropout, LeakyReLU\n",
"from tensorflow.keras.models import Model\n",
"\n",
"\n",
"def seq_model():\n",
" model = Sequential()\n",
" model.add(Input(shape=(1094,)))\n",
" model.add(Dense(512, activation='relu'))\n",
" model.add(Dense(256, activation='relu'))\n",
" model.add(Dense(128, activation='relu'))\n",
" model.add(Dense(32, activation='relu'))\n",
" model.add(Dense(4, activation='softmax'))\n",
" return model\n",
"\n",
"\n",
"def dual_input_model():\n",
" \n",
" inp1 = Input(shape=(1094,))\n",
" inp2 = Input(shape=(1094,))\n",
"\n",
" x2 = Dense(512, activation='relu')(inp1)\n",
" x1 = Dense(512, activation='relu')(x1)\n",
" x1 = Dense(256, activation='relu')(x1)\n",
" x1 = Dense(128, activation='relu')(x1)\n",
"\n",
" x2 = Dense(512, activation='relu')(inp2)\n",
" x2 = Dense(512, activation='relu')(x2)\n",
" x2 = Dense(256, activation='relu')(x2)\n",
" x2 = Dense(128, activation='relu')(x2)\n",
"\n",
" x = Maximum()([x1,x2])\n",
" x = Dense(256, activation='relu')(x)\n",
" x = Dense(64, activation='relu')(x)\n",
" out = Dense(4, activation='softmax')(x)\n",
"\n",
" model = Model([inp1, inp2], out)\n",
" return model\n",
"\n",
"\n",
"class ClassWeightMult(tf.keras.layers.Layer):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.class_weight = class_weight\n",
"\n",
" def call(self, inputs):\n",
" return inputs * self.class_weight\n",
"\n",
"\n",
"def deep_linear_network():\n",
" \n",
" inp = Input(shape=(1094,))\n",
" act = 'tanh'\n",
" alpha = -0.5\n",
"\n",
" x = Dense(2048)(inp)\n",
" x = LeakyReLU(alpha=alpha)(x)\n",
" x = Dense(1024)(x)\n",
" x = LeakyReLU(alpha=alpha)(x)\n",
"\n",
" x = Dropout(0.5)(x)\n",
"\n",
" x = Dense(512)(x)\n",
" x = LeakyReLU(alpha=alpha)(x)\n",
" x = Dense(256)(x)\n",
" x = LeakyReLU(alpha=alpha)(x)\n",
"\n",
" x = Dropout(0.5)(x)\n",
"\n",
" x = Dense(128)(x)\n",
" x = LeakyReLU(alpha=alpha)(x)\n",
" x = Dense(64)(x)\n",
" x = LeakyReLU(alpha=alpha)(x)\n",
"\n",
" x = Dense(4, activation='softmax')(x)\n",
" out = ClassWeightMult()(x)\n",
"\n",
" model = Model(inp, out)\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model_19\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" input_23 (InputLayer) [(None, 1094)] 0 \n",
" \n",
" dense_272 (Dense) (None, 2048) 2242560 \n",
" \n",
" leaky_re_lu_75 (LeakyReLU) (None, 2048) 0 \n",
" \n",
" dense_273 (Dense) (None, 1024) 2098176 \n",
" \n",
" leaky_re_lu_76 (LeakyReLU) (None, 1024) 0 \n",
" \n",
" dropout_57 (Dropout) (None, 1024) 0 \n",
" \n",
" dense_274 (Dense) (None, 512) 524800 \n",
" \n",
" leaky_re_lu_77 (LeakyReLU) (None, 512) 0 \n",
" \n",
" dense_275 (Dense) (None, 256) 131328 \n",
" \n",
" leaky_re_lu_78 (LeakyReLU) (None, 256) 0 \n",
" \n",
" dropout_58 (Dropout) (None, 256) 0 \n",
" \n",
" dense_276 (Dense) (None, 128) 32896 \n",
" \n",
" leaky_re_lu_79 (LeakyReLU) (None, 128) 0 \n",
" \n",
" dense_277 (Dense) (None, 64) 8256 \n",
" \n",
" leaky_re_lu_80 (LeakyReLU) (None, 64) 0 \n",
" \n",
" dense_278 (Dense) (None, 4) 260 \n",
" \n",
" class_weight_mult_19 (Class (None, 4) 0 \n",
" WeightMult) \n",
" \n",
"=================================================================\n",
"Total params: 5,038,276\n",
"Trainable params: 5,038,276\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n",
"You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model/model_to_dot to work.\n"
]
}
],
"source": [
"from tensorflow.keras.utils import plot_model\n",
"\n",
"'''\n",
"model = seq_model()\n",
"model.summary()\n",
"\n",
"model = dual_input_model()\n",
"model.summary()\n",
"'''\n",
"\n",
"model = deep_linear_network()\n",
"model.summary()\n",
"plot_model(model, 'model.png', show_shapes=True)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras import optimizers\n",
"import tensorflow_addons as tfa\n",
"\n",
"adam = optimizers.Adam(learning_rate=1e-3, beta_1=0.9, beta_2=0.999, epsilon=1e-07, decay=0.0)\n",
"model.compile(\n",
" optimizer=adam, \n",
" loss=tf.keras.losses.CategoricalCrossentropy(),\n",
" # loss=tfa.losses.WeightedKappaLoss(\n",
" # num_classes=4,\n",
" # ),\n",
" metrics=[\n",
" tfa.metrics.CohenKappa(num_classes=4, weightage='quadratic'),\n",
" tfa.metrics.F1Score(num_classes=4),\n",
" 'accuracy'\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/15\n",
"374/374 [==============================] - ETA: 0s - loss: 1.5278 - cohen_kappa: 0.0603 - f1_score: 0.2545 - accuracy: 0.4141\n",
"Epoch 1: val_cohen_kappa improved from -inf to 0.15863, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 13s 32ms/step - loss: 1.5278 - cohen_kappa: 0.0603 - f1_score: 0.2545 - accuracy: 0.4141 - val_loss: 1.1208 - val_cohen_kappa: 0.1586 - val_f1_score: 0.2505 - val_accuracy: 0.5230 - lr: 0.0010\n",
"Epoch 2/15\n",
"374/374 [==============================] - ETA: 0s - loss: 1.1356 - cohen_kappa: 0.4942 - f1_score: 0.3918 - accuracy: 0.5725\n",
"Epoch 2: val_cohen_kappa improved from 0.15863 to 0.60632, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 10s 27ms/step - loss: 1.1356 - cohen_kappa: 0.4942 - f1_score: 0.3918 - accuracy: 0.5725 - val_loss: 0.9759 - val_cohen_kappa: 0.6063 - val_f1_score: 0.3626 - val_accuracy: 0.5999 - lr: 0.0010\n",
"Epoch 3/15\n",
"372/374 [============================>.] - ETA: 0s - loss: 1.2932 - cohen_kappa: 0.5757 - f1_score: 0.4185 - accuracy: 0.5983\n",
"Epoch 3: val_cohen_kappa improved from 0.60632 to 0.61485, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 10s 27ms/step - loss: 1.2914 - cohen_kappa: 0.5759 - f1_score: 0.4187 - accuracy: 0.5979 - val_loss: 1.1429 - val_cohen_kappa: 0.6149 - val_f1_score: 0.3919 - val_accuracy: 0.4375 - lr: 0.0010\n",
"Epoch 4/15\n",
"374/374 [==============================] - ETA: 0s - loss: 0.8923 - cohen_kappa: 0.6951 - f1_score: 0.4450 - accuracy: 0.6479\n",
"Epoch 4: val_cohen_kappa improved from 0.61485 to 0.63428, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 11s 29ms/step - loss: 0.8923 - cohen_kappa: 0.6951 - f1_score: 0.4450 - accuracy: 0.6479 - val_loss: 1.0035 - val_cohen_kappa: 0.6343 - val_f1_score: 0.5056 - val_accuracy: 0.6794 - lr: 0.0010\n",
"Epoch 5/15\n",
"373/374 [============================>.] - ETA: 0s - loss: 0.7918 - cohen_kappa: 0.7419 - f1_score: 0.5035 - accuracy: 0.7014\n",
"Epoch 5: val_cohen_kappa improved from 0.63428 to 0.68174, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 10s 28ms/step - loss: 0.7921 - cohen_kappa: 0.7411 - f1_score: 0.5028 - accuracy: 0.7007 - val_loss: 0.9548 - val_cohen_kappa: 0.6817 - val_f1_score: 0.4058 - val_accuracy: 0.6460 - lr: 0.0010\n",
"Epoch 6/15\n",
"373/374 [============================>.] - ETA: 0s - loss: 0.7112 - cohen_kappa: 0.7750 - f1_score: 0.5736 - accuracy: 0.7550\n",
"Epoch 6: val_cohen_kappa did not improve from 0.68174\n",
"374/374 [==============================] - 9s 25ms/step - loss: 0.7110 - cohen_kappa: 0.7747 - f1_score: 0.5735 - accuracy: 0.7548 - val_loss: 0.8870 - val_cohen_kappa: 0.5534 - val_f1_score: 0.5383 - val_accuracy: 0.6747 - lr: 0.0010\n",
"Epoch 7/15\n",
"374/374 [==============================] - ETA: 0s - loss: 0.6895 - cohen_kappa: 0.7905 - f1_score: 0.6018 - accuracy: 0.7776\n",
"Epoch 7: val_cohen_kappa improved from 0.68174 to 0.69411, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 11s 28ms/step - loss: 0.6895 - cohen_kappa: 0.7905 - f1_score: 0.6018 - accuracy: 0.7776 - val_loss: 0.9165 - val_cohen_kappa: 0.6941 - val_f1_score: 0.5543 - val_accuracy: 0.7415 - lr: 0.0010\n",
"Epoch 8/15\n",
"374/374 [==============================] - ETA: 0s - loss: 0.8152 - cohen_kappa: 0.7720 - f1_score: 0.5982 - accuracy: 0.7655\n",
"Epoch 8: val_cohen_kappa improved from 0.69411 to 0.72625, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 10s 27ms/step - loss: 0.8152 - cohen_kappa: 0.7720 - f1_score: 0.5982 - accuracy: 0.7655 - val_loss: 0.9892 - val_cohen_kappa: 0.7262 - val_f1_score: 0.5843 - val_accuracy: 0.6880 - lr: 0.0010\n",
"Epoch 9/15\n",
"373/374 [============================>.] - ETA: 0s - loss: 0.7435 - cohen_kappa: 0.7748 - f1_score: 0.6210 - accuracy: 0.7736\n",
"Epoch 9: val_cohen_kappa did not improve from 0.72625\n",
"374/374 [==============================] - 9s 23ms/step - loss: 0.7461 - cohen_kappa: 0.7743 - f1_score: 0.6205 - accuracy: 0.7732 - val_loss: 1.0310 - val_cohen_kappa: 0.4987 - val_f1_score: 0.5052 - val_accuracy: 0.6406 - lr: 0.0010\n",
"Epoch 10/15\n",
"374/374 [==============================] - ETA: 0s - loss: 0.6038 - cohen_kappa: 0.8232 - f1_score: 0.6674 - accuracy: 0.8107\n",
"Epoch 10: val_cohen_kappa did not improve from 0.72625\n",
"374/374 [==============================] - 8s 23ms/step - loss: 0.6038 - cohen_kappa: 0.8232 - f1_score: 0.6674 - accuracy: 0.8107 - val_loss: 0.8689 - val_cohen_kappa: 0.6974 - val_f1_score: 0.5536 - val_accuracy: 0.7422 - lr: 0.0010\n",
"Epoch 11/15\n",
"372/374 [============================>.] - ETA: 0s - loss: 0.6978 - cohen_kappa: 0.8404 - f1_score: 0.6702 - accuracy: 0.8066\n",
"Epoch 11: val_cohen_kappa did not improve from 0.72625\n",
"374/374 [==============================] - 9s 24ms/step - loss: 0.6970 - cohen_kappa: 0.8405 - f1_score: 0.6700 - accuracy: 0.8067 - val_loss: 1.0435 - val_cohen_kappa: 0.6793 - val_f1_score: 0.5586 - val_accuracy: 0.7375 - lr: 0.0010\n",
"Epoch 12/15\n",
"374/374 [==============================] - ETA: 0s - loss: 0.5071 - cohen_kappa: 0.8627 - f1_score: 0.7161 - accuracy: 0.8372\n",
"Epoch 12: val_cohen_kappa did not improve from 0.72625\n",
"374/374 [==============================] - 9s 23ms/step - loss: 0.5071 - cohen_kappa: 0.8627 - f1_score: 0.7161 - accuracy: 0.8372 - val_loss: 0.9421 - val_cohen_kappa: 0.7231 - val_f1_score: 0.5676 - val_accuracy: 0.7535 - lr: 0.0010\n",
"Epoch 13/15\n",
"372/374 [============================>.] - ETA: 0s - loss: 0.5068 - cohen_kappa: 0.8596 - f1_score: 0.7302 - accuracy: 0.8424\n",
"Epoch 13: val_cohen_kappa did not improve from 0.72625\n",
"374/374 [==============================] - 9s 23ms/step - loss: 0.5091 - cohen_kappa: 0.8590 - f1_score: 0.7293 - accuracy: 0.8417 - val_loss: 1.0945 - val_cohen_kappa: 0.7230 - val_f1_score: 0.5801 - val_accuracy: 0.6212 - lr: 0.0010\n",
"Epoch 14/15\n",
"373/374 [============================>.] - ETA: 0s - loss: 0.4725 - cohen_kappa: 0.8820 - f1_score: 0.7482 - accuracy: 0.8527\n",
"Epoch 14: val_cohen_kappa did not improve from 0.72625\n",
"374/374 [==============================] - 9s 23ms/step - loss: 0.4747 - cohen_kappa: 0.8813 - f1_score: 0.7489 - accuracy: 0.8524 - val_loss: 1.1694 - val_cohen_kappa: 0.6641 - val_f1_score: 0.5649 - val_accuracy: 0.6339 - lr: 0.0010\n",
"Epoch 15/15\n",
"372/374 [============================>.] - ETA: 0s - loss: 0.7099 - cohen_kappa: 0.8074 - f1_score: 0.7076 - accuracy: 0.8138\n",
"Epoch 15: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
"\n",
"Epoch 15: val_cohen_kappa did not improve from 0.72625\n",
"374/374 [==============================] - 9s 23ms/step - loss: 0.7078 - cohen_kappa: 0.8081 - f1_score: 0.7076 - accuracy: 0.8138 - val_loss: 1.0812 - val_cohen_kappa: 0.7147 - val_f1_score: 0.5640 - val_accuracy: 0.7528 - lr: 0.0010\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x492717d30>"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss',\n",
" factor=0.5,\n",
" patience=5,\n",
" verbose=1,\n",
" min_lr=1e-8,\n",
")\n",
"\n",
"checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
" 'models/checkpoint',\n",
" monitor='val_cohen_kappa',\n",
" verbose=1,\n",
" save_best_only=True,\n",
" mode='max',\n",
" save_weights_only=False\n",
")\n",
"\n",
"\n",
"model.fit(\n",
" x=X_train,\n",
" y=y_train_categorical,\n",
" batch_size=16,\n",
" epochs=15,\n",
" verbose=1,\n",
" validation_data=(X_test, y_test_categorical),\n",
" shuffle=True,\n",
" callbacks=[\n",
" reduce_lr,\n",
" checkpoint\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cohen kappa score: 0.7147002691877022 \n",
"f1_score: 0.7161477740976335\n"
]
}
],
"source": [
"from sklearn.metrics import cohen_kappa_score, f1_score\n",
"\n",
"y_prob = model.predict(X_test) \n",
"y_classes = y_prob.argmax(axis=-1)\n",
"\n",
"ck_score = cohen_kappa_score(\n",
" y_test,\n",
" y_classes,\n",
" weights='quadratic'\n",
")\n",
"\n",
"f1_score = f1_score(\n",
" y_test,\n",
" y_classes,\n",
" labels=[0,1,2,3],\n",
" average='weighted',\n",
")\n",
"\n",
"print(\n",
" 'cohen kappa score:', ck_score,\n",
" '\\nf1_score:', f1_score\n",
")\n"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras import Sequential\n",
"from tensorflow.keras.layers import Dense, Input, Concatenate, Maximum, Dropout, LeakyReLU\n",
"from tensorflow.keras.models import Model\n",
"\n",
"class ClassWeightMult(tf.keras.layers.Layer):\n",
" def __init__(self):\n",
" super().__init__()\n",
" self.class_weight = class_weight\n",
"\n",
" def call(self, inputs):\n",
" return inputs * self.class_weight\n",
"\n",
"\n",
"def xtanh(x):\n",
" return x * tf.keras.activations.tanh(x)\n",
"\n",
"\n",
"def xsigmoid(x):\n",
" return tf.keras.activations.swish(x)\n",
"\n",
"\n",
"def branchy_linear_network():\n",
" \n",
" inp = Input(shape=(1094,))\n",
" swish = tf.keras.activations.swish\n",
" x = Dense(2048)(inp)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
" x = Dense(2048)(x)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
" x = Dense(2048)(x)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
" x = Dropout(0.5)(x)\n",
"\n",
" x2 = Dense(512)(x)\n",
" x2 = xtanh(x2)\n",
" x3 = Dense(512)(x)\n",
" x3 = swish(x3)\n",
"\n",
" x2 = Dense(512)(x3)\n",
" x3 = Dense(512)(x2)\n",
" x2 = xtanh(x2)\n",
" x3 = swish(x3)\n",
"\n",
" x3 = Dense(512)(x2)\n",
" x2 = Dense(512)(x3)\n",
" x2 = xtanh(x2)\n",
" x3 = swish(x3)\n",
"\n",
" x = Maximum()([x2,x3])\n",
" x = Dropout(0.5)(x)\n",
"\n",
" x = Dense(128)(x)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
" x = Dense(128)(x)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
" x = Dense(128)(x)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
"\n",
" x = Dropout(0.5)(x)\n",
"\n",
" x = Dense(4, activation='softmax')(x)\n",
" out = ClassWeightMult()(x)\n",
"\n",
" model = Model(inp, out)\n",
" return model\n",
"\n",
"\n",
"def simple_branchy_linear_network():\n",
" \n",
" inp = Input(shape=(1094,))\n",
" x = Dense(2048)(inp)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
" x = Dense(2048)(x)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
" x = Dropout(0.5)(x)\n",
"\n",
" x1 = Dense(1024)(x)\n",
" x1 = xtanh(x1)\n",
" x1 = Dense(1024)(x1)\n",
" x1 = xtanh(x1)\n",
" x1 = Dense(128)(x1)\n",
" x1 = xtanh(x1)\n",
" x1 = Dropout(0.5)(x1)\n",
"\n",
" x2 = Dense(1024)(x)\n",
" x2 = xsigmoid(x2)\n",
" x2 = Dense(512)(x2)\n",
" x2 = xsigmoid(x2)\n",
" x2 = Dense(128)(x2)\n",
" x2 = xsigmoid(x2)\n",
" x2 = Dropout(0.5)(x2)\n",
"\n",
" x = Concatenate()([x1, x2])\n",
"\n",
" x = Dense(128)(x)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
" x = Dense(32)(x)\n",
" x = LeakyReLU(alpha=0.5)(x)\n",
"\n",
" x = Dropout(0.5)(x)\n",
"\n",
" x = Dense(4, activation='softmax')(x)\n",
" out = ClassWeightMult()(x)\n",
"\n",
" model = Model(inp, out)\n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"model_18\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
" input_22 (InputLayer) [(None, 1094)] 0 [] \n",
" \n",
" dense_261 (Dense) (None, 2048) 2242560 ['input_22[0][0]'] \n",
" \n",
" leaky_re_lu_71 (LeakyReLU) (None, 2048) 0 ['dense_261[0][0]'] \n",
" \n",
" dense_262 (Dense) (None, 2048) 4196352 ['leaky_re_lu_71[0][0]'] \n",
" \n",
" leaky_re_lu_72 (LeakyReLU) (None, 2048) 0 ['dense_262[0][0]'] \n",
" \n",
" dropout_53 (Dropout) (None, 2048) 0 ['leaky_re_lu_72[0][0]'] \n",
" \n",
" dense_263 (Dense) (None, 1024) 2098176 ['dropout_53[0][0]'] \n",
" \n",
" tf.math.tanh_57 (TFOpLambda) (None, 1024) 0 ['dense_263[0][0]'] \n",
" \n",
" tf.math.multiply_71 (TFOpLambd (None, 1024) 0 ['dense_263[0][0]', \n",
" a) 'tf.math.tanh_57[0][0]'] \n",
" \n",
" dense_264 (Dense) (None, 1024) 1049600 ['tf.math.multiply_71[0][0]'] \n",
" \n",
" dense_266 (Dense) (None, 1024) 2098176 ['dropout_53[0][0]'] \n",
" \n",
" tf.math.tanh_58 (TFOpLambda) (None, 1024) 0 ['dense_264[0][0]'] \n",
" \n",
" tf.nn.silu_66 (TFOpLambda) (None, 1024) 0 ['dense_266[0][0]'] \n",
" \n",
" tf.math.multiply_72 (TFOpLambd (None, 1024) 0 ['dense_264[0][0]', \n",
" a) 'tf.math.tanh_58[0][0]'] \n",
" \n",
" dense_267 (Dense) (None, 512) 524800 ['tf.nn.silu_66[0][0]'] \n",
" \n",
" dense_265 (Dense) (None, 128) 131200 ['tf.math.multiply_72[0][0]'] \n",
" \n",
" tf.nn.silu_67 (TFOpLambda) (None, 512) 0 ['dense_267[0][0]'] \n",
" \n",
" tf.math.tanh_59 (TFOpLambda) (None, 128) 0 ['dense_265[0][0]'] \n",
" \n",
" dense_268 (Dense) (None, 128) 65664 ['tf.nn.silu_67[0][0]'] \n",
" \n",
" tf.math.multiply_73 (TFOpLambd (None, 128) 0 ['dense_265[0][0]', \n",
" a) 'tf.math.tanh_59[0][0]'] \n",
" \n",
" tf.nn.silu_68 (TFOpLambda) (None, 128) 0 ['dense_268[0][0]'] \n",
" \n",
" dropout_54 (Dropout) (None, 128) 0 ['tf.math.multiply_73[0][0]'] \n",
" \n",
" dropout_55 (Dropout) (None, 128) 0 ['tf.nn.silu_68[0][0]'] \n",
" \n",
" concatenate_10 (Concatenate) (None, 256) 0 ['dropout_54[0][0]', \n",
" 'dropout_55[0][0]'] \n",
" \n",
" dense_269 (Dense) (None, 128) 32896 ['concatenate_10[0][0]'] \n",
" \n",
" leaky_re_lu_73 (LeakyReLU) (None, 128) 0 ['dense_269[0][0]'] \n",
" \n",
" dense_270 (Dense) (None, 32) 4128 ['leaky_re_lu_73[0][0]'] \n",
" \n",
" leaky_re_lu_74 (LeakyReLU) (None, 32) 0 ['dense_270[0][0]'] \n",
" \n",
" dropout_56 (Dropout) (None, 32) 0 ['leaky_re_lu_74[0][0]'] \n",
" \n",
" dense_271 (Dense) (None, 4) 132 ['dropout_56[0][0]'] \n",
" \n",
" class_weight_mult_18 (ClassWei (None, 4) 0 ['dense_271[0][0]'] \n",
" ghtMult) \n",
" \n",
"==================================================================================================\n",
"Total params: 12,443,684\n",
"Trainable params: 12,443,684\n",
"Non-trainable params: 0\n",
"__________________________________________________________________________________________________\n",
"You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model/model_to_dot to work.\n"
]
}
],
"source": [
"from tensorflow.keras.utils import plot_model\n",
"\n",
"model = simple_branchy_linear_network()\n",
"model.summary()\n",
"plot_model(model, 'branchy_linear_network.png', show_shapes=True)"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras import optimizers\n",
"import tensorflow_addons as tfa\n",
"\n",
"adam = optimizers.Adam(learning_rate=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)\n",
"model.compile(\n",
" optimizer=adam, \n",
" loss=tf.keras.losses.CategoricalCrossentropy(),\n",
" metrics=[\n",
" tfa.metrics.CohenKappa(num_classes=4, weightage='quadratic'),\n",
" tfa.metrics.F1Score(num_classes=4),\n",
" 'accuracy'\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/12\n",
"373/374 [============================>.] - ETA: 0s - loss: 0.4662 - cohen_kappa: 0.8101 - f1_score: 0.7373 - accuracy: 0.8251\n",
"Epoch 1: val_cohen_kappa improved from -inf to 0.66027, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 16s 39ms/step - loss: 0.4657 - cohen_kappa: 0.8103 - f1_score: 0.7378 - accuracy: 0.8254 - val_loss: 0.9084 - val_cohen_kappa: 0.6603 - val_f1_score: 0.5964 - val_accuracy: 0.7188 - lr: 1.0000e-04\n",
"Epoch 2/12\n",
"374/374 [==============================] - ETA: 0s - loss: 0.4305 - cohen_kappa: 0.8292 - f1_score: 0.7470 - accuracy: 0.8361\n",
"Epoch 2: val_cohen_kappa improved from 0.66027 to 0.69849, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 14s 36ms/step - loss: 0.4305 - cohen_kappa: 0.8292 - f1_score: 0.7470 - accuracy: 0.8361 - val_loss: 0.8890 - val_cohen_kappa: 0.6985 - val_f1_score: 0.6061 - val_accuracy: 0.7395 - lr: 1.0000e-04\n",
"Epoch 3/12\n",
"372/374 [============================>.] - ETA: 0s - loss: 0.4124 - cohen_kappa: 0.8330 - f1_score: 0.7614 - accuracy: 0.8422\n",
"Epoch 3: val_cohen_kappa improved from 0.69849 to 0.70371, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 12s 33ms/step - loss: 0.4125 - cohen_kappa: 0.8328 - f1_score: 0.7609 - accuracy: 0.8421 - val_loss: 0.9561 - val_cohen_kappa: 0.7037 - val_f1_score: 0.6213 - val_accuracy: 0.7368 - lr: 1.0000e-04\n",
"Epoch 4/12\n",
"373/374 [============================>.] - ETA: 0s - loss: 0.4073 - cohen_kappa: 0.8329 - f1_score: 0.7724 - accuracy: 0.8482\n",
"Epoch 4: val_cohen_kappa improved from 0.70371 to 0.71820, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 13s 35ms/step - loss: 0.4074 - cohen_kappa: 0.8332 - f1_score: 0.7719 - accuracy: 0.8481 - val_loss: 0.9655 - val_cohen_kappa: 0.7182 - val_f1_score: 0.6272 - val_accuracy: 0.7295 - lr: 1.0000e-04\n",
"Epoch 5/12\n",
"374/374 [==============================] - ETA: 0s - loss: 0.3683 - cohen_kappa: 0.8537 - f1_score: 0.7891 - accuracy: 0.8600\n",
"Epoch 5: val_cohen_kappa improved from 0.71820 to 0.72140, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 14s 37ms/step - loss: 0.3683 - cohen_kappa: 0.8537 - f1_score: 0.7891 - accuracy: 0.8600 - val_loss: 0.9610 - val_cohen_kappa: 0.7214 - val_f1_score: 0.6293 - val_accuracy: 0.7448 - lr: 1.0000e-04\n",
"Epoch 6/12\n",
"374/374 [==============================] - ETA: 0s - loss: 0.3793 - cohen_kappa: 0.8452 - f1_score: 0.7825 - accuracy: 0.8546\n",
"Epoch 6: val_cohen_kappa did not improve from 0.72140\n",
"374/374 [==============================] - 11s 28ms/step - loss: 0.3793 - cohen_kappa: 0.8452 - f1_score: 0.7825 - accuracy: 0.8546 - val_loss: 0.9843 - val_cohen_kappa: 0.6822 - val_f1_score: 0.6362 - val_accuracy: 0.7234 - lr: 1.0000e-04\n",
"Epoch 7/12\n",
"373/374 [============================>.] - ETA: 0s - loss: 0.3484 - cohen_kappa: 0.8638 - f1_score: 0.8009 - accuracy: 0.8673\n",
"Epoch 7: ReduceLROnPlateau reducing learning rate to 4.999999873689376e-05.\n",
"\n",
"Epoch 7: val_cohen_kappa did not improve from 0.72140\n",
"374/374 [==============================] - 11s 28ms/step - loss: 0.3481 - cohen_kappa: 0.8634 - f1_score: 0.8008 - accuracy: 0.8673 - val_loss: 0.9808 - val_cohen_kappa: 0.7010 - val_f1_score: 0.6250 - val_accuracy: 0.7368 - lr: 1.0000e-04\n",
"Epoch 8/12\n",
"372/374 [============================>.] - ETA: 0s - loss: 0.2979 - cohen_kappa: 0.8864 - f1_score: 0.8322 - accuracy: 0.8889\n",
"Epoch 8: val_cohen_kappa did not improve from 0.72140\n",
"374/374 [==============================] - 11s 29ms/step - loss: 0.2974 - cohen_kappa: 0.8866 - f1_score: 0.8325 - accuracy: 0.8892 - val_loss: 1.0621 - val_cohen_kappa: 0.7187 - val_f1_score: 0.6319 - val_accuracy: 0.7462 - lr: 5.0000e-05\n",
"Epoch 9/12\n",
"374/374 [==============================] - ETA: 0s - loss: 0.2735 - cohen_kappa: 0.8961 - f1_score: 0.8363 - accuracy: 0.8956\n",
"Epoch 9: val_cohen_kappa did not improve from 0.72140\n",
"374/374 [==============================] - 11s 28ms/step - loss: 0.2735 - cohen_kappa: 0.8961 - f1_score: 0.8363 - accuracy: 0.8956 - val_loss: 1.1085 - val_cohen_kappa: 0.7117 - val_f1_score: 0.6264 - val_accuracy: 0.7415 - lr: 5.0000e-05\n",
"Epoch 10/12\n",
"373/374 [============================>.] - ETA: 0s - loss: 0.2609 - cohen_kappa: 0.9017 - f1_score: 0.8462 - accuracy: 0.8998\n",
"Epoch 10: val_cohen_kappa did not improve from 0.72140\n",
"374/374 [==============================] - 11s 29ms/step - loss: 0.2611 - cohen_kappa: 0.9019 - f1_score: 0.8464 - accuracy: 0.8997 - val_loss: 1.1693 - val_cohen_kappa: 0.7109 - val_f1_score: 0.6355 - val_accuracy: 0.7301 - lr: 5.0000e-05\n",
"Epoch 11/12\n",
"374/374 [==============================] - ETA: 0s - loss: 0.2529 - cohen_kappa: 0.9042 - f1_score: 0.8510 - accuracy: 0.9022\n",
"Epoch 11: val_cohen_kappa did not improve from 0.72140\n",
"374/374 [==============================] - 10s 28ms/step - loss: 0.2529 - cohen_kappa: 0.9042 - f1_score: 0.8510 - accuracy: 0.9022 - val_loss: 1.1396 - val_cohen_kappa: 0.7132 - val_f1_score: 0.6194 - val_accuracy: 0.7368 - lr: 5.0000e-05\n",
"Epoch 12/12\n",
"373/374 [============================>.] - ETA: 0s - loss: 0.2456 - cohen_kappa: 0.9056 - f1_score: 0.8579 - accuracy: 0.9067\n",
"Epoch 12: ReduceLROnPlateau reducing learning rate to 2.499999936844688e-05.\n",
"\n",
"Epoch 12: val_cohen_kappa improved from 0.72140 to 0.72985, saving model to models/checkpoint\n",
"INFO:tensorflow:Assets written to: models/checkpoint/assets\n",
"374/374 [==============================] - 12s 33ms/step - loss: 0.2454 - cohen_kappa: 0.9054 - f1_score: 0.8581 - accuracy: 0.9068 - val_loss: 1.2013 - val_cohen_kappa: 0.7299 - val_f1_score: 0.6246 - val_accuracy: 0.7395 - lr: 5.0000e-05\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x47b9925b0>"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n",
" monitor='val_loss',\n",
" factor=0.5,\n",
" patience=5,\n",
" verbose=1,\n",
" min_lr=1e-8,\n",
")\n",
"\n",
"checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
" 'models/checkpoint',\n",
" monitor='val_cohen_kappa',\n",
" verbose=1,\n",
" save_best_only=True,\n",
" mode='max',\n",
" save_weights_only=False\n",
")\n",
"\n",
"model.fit(\n",
" x=X_train,\n",
" y=y_train_categorical,\n",
" batch_size=16,\n",
" epochs=12,\n",
" verbose=1,\n",
" validation_data=(X_test, y_test_categorical),\n",
" shuffle=True,\n",
" callbacks=[\n",
" reduce_lr,\n",
" checkpoint\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cohen kappa score: 0.7298539158990368 \n",
"f1_score: 0.7321093589151875\n"
]
}
],
"source": [
"from sklearn.metrics import cohen_kappa_score, f1_score\n",
"\n",
"# load the best model\n",
"model.load_weights('models/checkpoint')\n",
"\n",
"y_prob = model.predict(X_test) \n",
"y_classes = y_prob.argmax(axis=-1)\n",
"\n",
"ck_score = cohen_kappa_score(\n",
" y_test,\n",
" y_classes,\n",
" weights='quadratic'\n",
")\n",
"\n",
"f1_score = f1_score(\n",
" y_test,\n",
" y_classes,\n",
" labels=[0,1,2,3],\n",
" average='weighted',\n",
")\n",
"\n",
"print(\n",
" 'cohen kappa score:', ck_score,\n",
" '\\nf1_score:', f1_score\n",
")"
]
}
],
"metadata": {
"interpreter": {
"hash": "7cc07eb52ae30fed050dcfdcd599b50753511a3bb1bf5f100744caacfc229681"
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"file_extension": ".py",
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