Spaces:
Runtime error
Runtime error
last question WIP
Browse files- midterm/Pipfile +17 -0
- midterm/Pipfile.lock +1374 -0
- midterm/README.md +0 -0
- midterm/data/01_raw/CBC_data.csv +0 -0
- midterm/midterm/__init__.py +0 -0
- midterm/midterm/take_at_home_(1).ipynb +1258 -0
- midterm/pyproject.toml +22 -0
- midterm/tests/__init__.py +0 -0
midterm/Pipfile
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[[source]]
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url = "https://pypi.org/simple"
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verify_ssl = true
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name = "pypi"
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[packages]
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black = "*"
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jupyterlab = "*"
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pandas = "*"
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scikit-learn = "*"
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numpy = "*"
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ipython = "*"
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[dev-packages]
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[requires]
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python_version = "3.10"
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midterm/Pipfile.lock
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@@ -0,0 +1,1374 @@
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| 1312 |
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| 1320 |
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| 1330 |
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| 1340 |
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| 1350 |
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| 1357 |
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| 1358 |
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| 1359 |
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| 1360 |
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"sha256:ff3ddedaa95284f4f22a92b362f658f3d92f272d8c0fa009051bd5490c4d5a04"
|
| 1361 |
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|
| 1362 |
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|
| 1363 |
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|
| 1364 |
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| 1365 |
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| 1366 |
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| 1367 |
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|
| 1368 |
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|
| 1369 |
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|
| 1370 |
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"version": "==0.8.2"
|
| 1371 |
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}
|
| 1372 |
+
},
|
| 1373 |
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"develop": {}
|
| 1374 |
+
}
|
midterm/README.md
ADDED
|
File without changes
|
midterm/data/01_raw/CBC_data.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
midterm/midterm/__init__.py
ADDED
|
File without changes
|
midterm/midterm/take_at_home_(1).ipynb
ADDED
|
@@ -0,0 +1,1258 @@
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|
| 1 |
+
{
|
| 2 |
+
"cells": [
|
| 3 |
+
{
|
| 4 |
+
"cell_type": "markdown",
|
| 5 |
+
"metadata": {
|
| 6 |
+
"id": "oZ6_2B0E1DAh"
|
| 7 |
+
},
|
| 8 |
+
"source": [
|
| 9 |
+
"# Midterm - Spring 2023\n",
|
| 10 |
+
"\n",
|
| 11 |
+
"## Problem 1: Take-at-home (45 points total)\n",
|
| 12 |
+
"\n",
|
| 13 |
+
"You are applying for a position at the data science team of USDA and you are given data associated with determining appropriate parasite treatment of canines. The suggested treatment options are determined based on a **logistic regression** model that predicts if the canine is infected with a parasite. \n",
|
| 14 |
+
"\n",
|
| 15 |
+
"The data is given in the site: https://data.world/ehales/grls-parasite-study/workspace/file?filename=CBC_data.csv and more specifically in the CBC_data.csv file. Login using you University Google account to access the data and the description that includes a paper on the study (**you dont need to read the paper to solve this problem**). Your target variable $y$ column is titled `parasite_status`. \n",
|
| 16 |
+
"\n",
|
| 17 |
+
"\n"
|
| 18 |
+
]
|
| 19 |
+
},
|
| 20 |
+
{
|
| 21 |
+
"cell_type": "markdown",
|
| 22 |
+
"metadata": {
|
| 23 |
+
"id": "Aq8bln4u1DAo"
|
| 24 |
+
},
|
| 25 |
+
"source": [
|
| 26 |
+
"### Question 1 - Feature Engineering (5 points)\n",
|
| 27 |
+
"\n",
|
| 28 |
+
"Write the posterior probability expressions for logistic regression for the problem you are given to solve."
|
| 29 |
+
]
|
| 30 |
+
},
|
| 31 |
+
{
|
| 32 |
+
"cell_type": "markdown",
|
| 33 |
+
"metadata": {
|
| 34 |
+
"id": "_kd85pkA1DA3"
|
| 35 |
+
},
|
| 36 |
+
"source": [
|
| 37 |
+
"$$p(y=1| \\mathbf{x}, \\mathbf w)= \\frac{p(\\mathbf{x}| y=1)p(y=1)}{p(\\mathbf{x}|y=1)p(y=1)+p(\\mathbf{x}|y=0)}=\\frac{1}{1+\\exp(-\\alpha)}=\\sigma(\\alpha)$$\n",
|
| 38 |
+
"\n",
|
| 39 |
+
"$$p(y=0| \\mathbf{x}, \\mathbf w)=1-p(y=1|\\mathbf{x}^{T}\\mathbf{w})=1-\\sigma(\\alpha)=\\sigma(-\\alpha)$$"
|
| 40 |
+
]
|
| 41 |
+
},
|
| 42 |
+
{
|
| 43 |
+
"cell_type": "markdown",
|
| 44 |
+
"metadata": {
|
| 45 |
+
"id": "Gh6Fi5hz1DA6"
|
| 46 |
+
},
|
| 47 |
+
"source": [
|
| 48 |
+
"\n",
|
| 49 |
+
"\n",
|
| 50 |
+
"### Question 2 - Decision Boundary (5 points)\n",
|
| 51 |
+
"\n",
|
| 52 |
+
"Write the expression for the decision boundary assuming that $p(y=1)=p(y=0)$. The decision boundary is the line that separates the two classes.\n",
|
| 53 |
+
"\n",
|
| 54 |
+
"\n",
|
| 55 |
+
"\n"
|
| 56 |
+
]
|
| 57 |
+
},
|
| 58 |
+
{
|
| 59 |
+
"cell_type": "markdown",
|
| 60 |
+
"metadata": {
|
| 61 |
+
"id": "HMr2tF_J1DA-"
|
| 62 |
+
},
|
| 63 |
+
"source": [
|
| 64 |
+
"$$p(y=1)=p(y=0)→\\sigma(\\alpha)=-\\sigma(\\alpha)→2\\sigma(\\alpha)=1→\\sigma(\\alpha)=0.5≡\\sigma(\\mathbf{w}^T\\mathbf{x})=0.5$$"
|
| 65 |
+
]
|
| 66 |
+
},
|
| 67 |
+
{
|
| 68 |
+
"cell_type": "markdown",
|
| 69 |
+
"metadata": {
|
| 70 |
+
"id": "750Hn0iC1DBA"
|
| 71 |
+
},
|
| 72 |
+
"source": [
|
| 73 |
+
"\n",
|
| 74 |
+
"\n",
|
| 75 |
+
"### Question 3 - Loss function (5 points)\n",
|
| 76 |
+
"\n",
|
| 77 |
+
"Write the expression of the loss as a function of $\\mathbf w$ that makes sense for you to use in this problem. \n",
|
| 78 |
+
"\n",
|
| 79 |
+
"NOTE: The loss will be a function that will include this function: \n",
|
| 80 |
+
"\n",
|
| 81 |
+
"$$\\sigma(a) = \\frac{1}{1+e^{-a}}$$\n",
|
| 82 |
+
"\n"
|
| 83 |
+
]
|
| 84 |
+
},
|
| 85 |
+
{
|
| 86 |
+
"cell_type": "markdown",
|
| 87 |
+
"metadata": {
|
| 88 |
+
"id": "jxiR0jEh1DBD"
|
| 89 |
+
},
|
| 90 |
+
"source": [
|
| 91 |
+
"$$\n",
|
| 92 |
+
"\\begin{align}\n",
|
| 93 |
+
"L_{CE} = -[\\sum_{i=1}^m \\{y_i\\ln \\hat{y}_i + (1-y_i)\\ln(1-\\hat{y}_i)\\}]\\\\\n",
|
| 94 |
+
"= -[\\sum_{i=1}^m\\{y_i\\ln\\frac{1}{1+\\exp(-\\mathbf{w}^T\\mathbf{x})}+(1-y_i)\\ln(1-\\frac{1}{1+\\exp(-\\mathbf{w}^T\\mathbf{x})})\\}] \\\\\n",
|
| 95 |
+
"= -[\\sum_{i=1}^m\\{y_i[\\ln\\frac{1}{1+\\exp(-\\alpha)}-\\ln(1-\\frac{1}{1+\\exp(-\\alpha)})]+\\ln(1-\\frac{1}{1+\\exp(-\\alpha)})\\}] \\\\\n",
|
| 96 |
+
"= -[\\sum_{i=1}^m\\{y_i\\mathbf{w}^T\\mathbf{x}-\\ln(1+\\exp(\\mathbf{w}^T\\mathbf{x}))\\}]\n",
|
| 97 |
+
"\\end{align}\n",
|
| 98 |
+
"$$\n"
|
| 99 |
+
]
|
| 100 |
+
},
|
| 101 |
+
{
|
| 102 |
+
"cell_type": "markdown",
|
| 103 |
+
"metadata": {
|
| 104 |
+
"id": "AW4xA4221DBF"
|
| 105 |
+
},
|
| 106 |
+
"source": [
|
| 107 |
+
"\n",
|
| 108 |
+
"### Question 4 - Gradient (5 points)\n",
|
| 109 |
+
"\n",
|
| 110 |
+
"Write the expression of the gradient of the loss with respect to the parameters - show all your work.\n",
|
| 111 |
+
"\n"
|
| 112 |
+
]
|
| 113 |
+
},
|
| 114 |
+
{
|
| 115 |
+
"cell_type": "markdown",
|
| 116 |
+
"metadata": {
|
| 117 |
+
"id": "bo0YDA0i1DBJ"
|
| 118 |
+
},
|
| 119 |
+
"source": [
|
| 120 |
+
"$$\n",
|
| 121 |
+
"\\begin{align}\n",
|
| 122 |
+
"\\nabla_\\mathbf w L_{CE} = \\nabla_\\mathbf{w}-[\\sum_{i=1}^m\\{y_i\\mathbf{w}^T\\mathbf{x}-ln(1+\\exp(\\mathbf{w}^T\\mathbf{x})\\}] \\\\\n",
|
| 123 |
+
"= [-\\sum_{i=1}^my_ix_i] + [\\sum_{i=1}^m\\frac{1}{1+\\exp(\\mathbf{w}^T\\mathbf{x})}\\exp(\\mathbf{w}^T\\mathbf{x})*x_i] \\\\\n",
|
| 124 |
+
"= [-\\sum_{i=1}^my_ix_i] + [\\sum_{i=1}^m(\\sigma(\\mathbf{w}^T\\mathbf{x}))*x_i] \\\\\n",
|
| 125 |
+
"= \\sum_{i=1}^m (\\sigma(\\mathbf{w}^T\\mathbf{x})-y_i)x_i= \\sum_{i=1}^m(\\hat{y}_i-y_i)x_i\n",
|
| 126 |
+
"\\end{align}\n",
|
| 127 |
+
"$$"
|
| 128 |
+
]
|
| 129 |
+
},
|
| 130 |
+
{
|
| 131 |
+
"cell_type": "markdown",
|
| 132 |
+
"metadata": {
|
| 133 |
+
"id": "BpUryvTT1DBM"
|
| 134 |
+
},
|
| 135 |
+
"source": [
|
| 136 |
+
"### Question 5 - Imbalanced dataset (10 points)\n",
|
| 137 |
+
"\n",
|
| 138 |
+
"You are now told that in the dataset \n",
|
| 139 |
+
"\n",
|
| 140 |
+
"$$p(y=0) >> p(y=1)$$\n",
|
| 141 |
+
"\n",
|
| 142 |
+
"Can you comment if the accuracy of Logistic Regression will be affected by such imbalance?\n",
|
| 143 |
+
"\n"
|
| 144 |
+
]
|
| 145 |
+
},
|
| 146 |
+
{
|
| 147 |
+
"cell_type": "markdown",
|
| 148 |
+
"metadata": {
|
| 149 |
+
"id": "TqdImYQf1DBP"
|
| 150 |
+
},
|
| 151 |
+
"source": [
|
| 152 |
+
"We know that the loss function heavily penalizes confident wrong decisions. We expect then, that the model will be strongly incentivized to predict 0 more frequently than 1, regardless of the true outcome, as this minimizes loss. This will cause more false negatives, and will need to be considered with regards to our ROC curve. The accuracy will be affected, as there are so few positive examples that the model cannot accurately learn them."
|
| 153 |
+
]
|
| 154 |
+
},
|
| 155 |
+
{
|
| 156 |
+
"cell_type": "markdown",
|
| 157 |
+
"metadata": {
|
| 158 |
+
"id": "FK1Su76R1DBS"
|
| 159 |
+
},
|
| 160 |
+
"source": [
|
| 161 |
+
"\n",
|
| 162 |
+
"### Question 6 - SGD (15 points)\n",
|
| 163 |
+
"\n",
|
| 164 |
+
"The interviewer was impressed with your answers and wants to test your programming skills. \n",
|
| 165 |
+
"\n",
|
| 166 |
+
"1. Use the dataset to train a logistic regressor that will predict the target variable $y$. \n",
|
| 167 |
+
"\n",
|
| 168 |
+
" 2. Report the harmonic mean of precision (p) and recall (r) i.e the [metric called $F_1$ score](https://en.wikipedia.org/wiki/F-score) that is calculated as shown below using a test dataset that is 20% of each group. Plot the $F_1$ score vs the iteration number $t$. \n",
|
| 169 |
+
"\n",
|
| 170 |
+
"$$F_1 = \\frac{2}{r^{-1} + p^{-1}}$$\n",
|
| 171 |
+
"\n",
|
| 172 |
+
"Your code includes hyperparameter optimization of the learning rate and mini batch size. Please learn about cross validation which is a splitting strategy for tuning models [here](https://scikit-learn.org/stable/modules/cross_validation.html).\n",
|
| 173 |
+
"\n",
|
| 174 |
+
"You are allowed to use any library you want to code this problem.\n",
|
| 175 |
+
"\n"
|
| 176 |
+
]
|
| 177 |
+
},
|
| 178 |
+
{
|
| 179 |
+
"cell_type": "code",
|
| 180 |
+
"execution_count": 1,
|
| 181 |
+
"metadata": {
|
| 182 |
+
"id": "cnxqYSvL1DBV"
|
| 183 |
+
},
|
| 184 |
+
"outputs": [],
|
| 185 |
+
"source": [
|
| 186 |
+
"# write your code here"
|
| 187 |
+
]
|
| 188 |
+
},
|
| 189 |
+
{
|
| 190 |
+
"cell_type": "code",
|
| 191 |
+
"execution_count": 2,
|
| 192 |
+
"metadata": {
|
| 193 |
+
"tags": []
|
| 194 |
+
},
|
| 195 |
+
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|
| 196 |
+
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|
| 197 |
+
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| 198 |
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|
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|
| 215 |
+
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|
| 216 |
+
" <th></th>\n",
|
| 217 |
+
" <th>ID</th>\n",
|
| 218 |
+
" <th>SEX</th>\n",
|
| 219 |
+
" <th>TYPEAREA</th>\n",
|
| 220 |
+
" <th>SEX.REPRO</th>\n",
|
| 221 |
+
" <th>REPRO.STATUS</th>\n",
|
| 222 |
+
" <th>AGE</th>\n",
|
| 223 |
+
" <th>PARASITE_STATUS</th>\n",
|
| 224 |
+
" <th>RBC</th>\n",
|
| 225 |
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" <th>HGB</th>\n",
|
| 226 |
+
" <th>WBC</th>\n",
|
| 227 |
+
" <th>EOS.CNT</th>\n",
|
| 228 |
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" <th>MONO.CNT</th>\n",
|
| 229 |
+
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|
| 230 |
+
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|
| 231 |
+
" <th>LYMP.CNT</th>\n",
|
| 232 |
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|
| 233 |
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|
| 234 |
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|
| 235 |
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|
| 236 |
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" <th>0</th>\n",
|
| 237 |
+
" <td>grls5ZUT2BYY</td>\n",
|
| 238 |
+
" <td>Male</td>\n",
|
| 239 |
+
" <td>Suburban</td>\n",
|
| 240 |
+
" <td>IntactMale</td>\n",
|
| 241 |
+
" <td>Intact</td>\n",
|
| 242 |
+
" <td>9</td>\n",
|
| 243 |
+
" <td>Negative</td>\n",
|
| 244 |
+
" <td>6.4</td>\n",
|
| 245 |
+
" <td>16.6</td>\n",
|
| 246 |
+
" <td>14.2</td>\n",
|
| 247 |
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" <td>142.0</td>\n",
|
| 248 |
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" <td>852.0</td>\n",
|
| 249 |
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" <td>6390.0</td>\n",
|
| 250 |
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" <td>210.0</td>\n",
|
| 251 |
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" <td>6816.0</td>\n",
|
| 252 |
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" </tr>\n",
|
| 253 |
+
" <tr>\n",
|
| 254 |
+
" <th>1</th>\n",
|
| 255 |
+
" <td>grls8DCONYUU</td>\n",
|
| 256 |
+
" <td>Female</td>\n",
|
| 257 |
+
" <td>Rural</td>\n",
|
| 258 |
+
" <td>NeuteredFemale</td>\n",
|
| 259 |
+
" <td>Neutered</td>\n",
|
| 260 |
+
" <td>6</td>\n",
|
| 261 |
+
" <td>Negative</td>\n",
|
| 262 |
+
" <td>4.8</td>\n",
|
| 263 |
+
" <td>12.5</td>\n",
|
| 264 |
+
" <td>10.0</td>\n",
|
| 265 |
+
" <td>400.0</td>\n",
|
| 266 |
+
" <td>300.0</td>\n",
|
| 267 |
+
" <td>4800.0</td>\n",
|
| 268 |
+
" <td>209.0</td>\n",
|
| 269 |
+
" <td>4500.0</td>\n",
|
| 270 |
+
" </tr>\n",
|
| 271 |
+
" <tr>\n",
|
| 272 |
+
" <th>2</th>\n",
|
| 273 |
+
" <td>grlsUC5R4PTT</td>\n",
|
| 274 |
+
" <td>Male</td>\n",
|
| 275 |
+
" <td>Suburban</td>\n",
|
| 276 |
+
" <td>IntactMale</td>\n",
|
| 277 |
+
" <td>Intact</td>\n",
|
| 278 |
+
" <td>14</td>\n",
|
| 279 |
+
" <td>Negative</td>\n",
|
| 280 |
+
" <td>6.2</td>\n",
|
| 281 |
+
" <td>17.3</td>\n",
|
| 282 |
+
" <td>9.5</td>\n",
|
| 283 |
+
" <td>190.0</td>\n",
|
| 284 |
+
" <td>475.0</td>\n",
|
| 285 |
+
" <td>7315.0</td>\n",
|
| 286 |
+
" <td>164.0</td>\n",
|
| 287 |
+
" <td>1520.0</td>\n",
|
| 288 |
+
" </tr>\n",
|
| 289 |
+
" <tr>\n",
|
| 290 |
+
" <th>3</th>\n",
|
| 291 |
+
" <td>grlsXUR2PY88</td>\n",
|
| 292 |
+
" <td>Male</td>\n",
|
| 293 |
+
" <td>Rural</td>\n",
|
| 294 |
+
" <td>IntactMale</td>\n",
|
| 295 |
+
" <td>Intact</td>\n",
|
| 296 |
+
" <td>6</td>\n",
|
| 297 |
+
" <td>Negative</td>\n",
|
| 298 |
+
" <td>5.4</td>\n",
|
| 299 |
+
" <td>13.8</td>\n",
|
| 300 |
+
" <td>14.1</td>\n",
|
| 301 |
+
" <td>1692.0</td>\n",
|
| 302 |
+
" <td>423.0</td>\n",
|
| 303 |
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" <td>7755.0</td>\n",
|
| 304 |
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" <td>254.0</td>\n",
|
| 305 |
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" <td>4230.0</td>\n",
|
| 306 |
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" </tr>\n",
|
| 307 |
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" <tr>\n",
|
| 308 |
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" <th>4</th>\n",
|
| 309 |
+
" <td>grlsTBZUF3GG</td>\n",
|
| 310 |
+
" <td>Female</td>\n",
|
| 311 |
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" <td>Rural</td>\n",
|
| 312 |
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" <td>IntactFemale</td>\n",
|
| 313 |
+
" <td>Intact</td>\n",
|
| 314 |
+
" <td>18</td>\n",
|
| 315 |
+
" <td>Negative</td>\n",
|
| 316 |
+
" <td>5.9</td>\n",
|
| 317 |
+
" <td>14.4</td>\n",
|
| 318 |
+
" <td>6.5</td>\n",
|
| 319 |
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" <td>390.0</td>\n",
|
| 320 |
+
" <td>130.0</td>\n",
|
| 321 |
+
" <td>2795.0</td>\n",
|
| 322 |
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" <td>213.0</td>\n",
|
| 323 |
+
" <td>3185.0</td>\n",
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| 324 |
+
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|
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+
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|
| 326 |
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|
| 327 |
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|
| 328 |
+
],
|
| 329 |
+
"text/plain": [
|
| 330 |
+
" ID SEX TYPEAREA SEX.REPRO REPRO.STATUS AGE \\\n",
|
| 331 |
+
"0 grls5ZUT2BYY Male Suburban IntactMale Intact 9 \n",
|
| 332 |
+
"1 grls8DCONYUU Female Rural NeuteredFemale Neutered 6 \n",
|
| 333 |
+
"2 grlsUC5R4PTT Male Suburban IntactMale Intact 14 \n",
|
| 334 |
+
"3 grlsXUR2PY88 Male Rural IntactMale Intact 6 \n",
|
| 335 |
+
"4 grlsTBZUF3GG Female Rural IntactFemale Intact 18 \n",
|
| 336 |
+
"\n",
|
| 337 |
+
" PARASITE_STATUS RBC HGB WBC EOS.CNT MONO.CNT NUT.CNT PL.CNT \\\n",
|
| 338 |
+
"0 Negative 6.4 16.6 14.2 142.0 852.0 6390.0 210.0 \n",
|
| 339 |
+
"1 Negative 4.8 12.5 10.0 400.0 300.0 4800.0 209.0 \n",
|
| 340 |
+
"2 Negative 6.2 17.3 9.5 190.0 475.0 7315.0 164.0 \n",
|
| 341 |
+
"3 Negative 5.4 13.8 14.1 1692.0 423.0 7755.0 254.0 \n",
|
| 342 |
+
"4 Negative 5.9 14.4 6.5 390.0 130.0 2795.0 213.0 \n",
|
| 343 |
+
"\n",
|
| 344 |
+
" LYMP.CNT \n",
|
| 345 |
+
"0 6816.0 \n",
|
| 346 |
+
"1 4500.0 \n",
|
| 347 |
+
"2 1520.0 \n",
|
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"3 4230.0 \n",
|
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"4 3185.0 "
|
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]
|
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},
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| 352 |
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"execution_count": 2,
|
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"metadata": {},
|
| 354 |
+
"output_type": "execute_result"
|
| 355 |
+
}
|
| 356 |
+
],
|
| 357 |
+
"source": [
|
| 358 |
+
"import pandas as pd\n",
|
| 359 |
+
"\n",
|
| 360 |
+
"df = pd.read_csv('../data/01_raw/CBC_data.csv')\n",
|
| 361 |
+
"df.head()"
|
| 362 |
+
]
|
| 363 |
+
},
|
| 364 |
+
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|
| 365 |
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|
| 366 |
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|
| 367 |
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|
| 368 |
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|
| 369 |
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},
|
| 370 |
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|
| 371 |
+
{
|
| 372 |
+
"name": "stderr",
|
| 373 |
+
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|
| 374 |
+
"text": [
|
| 375 |
+
"/tmp/ipykernel_34276/1867621695.py:5: SettingWithCopyWarning: \n",
|
| 376 |
+
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
| 377 |
+
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
| 378 |
+
"\n",
|
| 379 |
+
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
| 380 |
+
" df2[x] = LabelEncoder().fit_transform(df2[x])\n",
|
| 381 |
+
"/tmp/ipykernel_34276/1867621695.py:5: SettingWithCopyWarning: \n",
|
| 382 |
+
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
| 383 |
+
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
| 384 |
+
"\n",
|
| 385 |
+
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
| 386 |
+
" df2[x] = LabelEncoder().fit_transform(df2[x])\n",
|
| 387 |
+
"/tmp/ipykernel_34276/1867621695.py:5: SettingWithCopyWarning: \n",
|
| 388 |
+
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
| 389 |
+
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
| 390 |
+
"\n",
|
| 391 |
+
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
| 392 |
+
" df2[x] = LabelEncoder().fit_transform(df2[x])\n"
|
| 393 |
+
]
|
| 394 |
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|
| 395 |
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|
| 396 |
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+
" }\n",
|
| 411 |
+
"</style>\n",
|
| 412 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
| 413 |
+
" <thead>\n",
|
| 414 |
+
" <tr style=\"text-align: right;\">\n",
|
| 415 |
+
" <th></th>\n",
|
| 416 |
+
" <th>SEX</th>\n",
|
| 417 |
+
" <th>TYPEAREA</th>\n",
|
| 418 |
+
" <th>SEX.REPRO</th>\n",
|
| 419 |
+
" <th>REPRO.STATUS</th>\n",
|
| 420 |
+
" <th>AGE</th>\n",
|
| 421 |
+
" <th>PARASITE_STATUS</th>\n",
|
| 422 |
+
" <th>RBC</th>\n",
|
| 423 |
+
" <th>HGB</th>\n",
|
| 424 |
+
" <th>WBC</th>\n",
|
| 425 |
+
" <th>EOS.CNT</th>\n",
|
| 426 |
+
" <th>MONO.CNT</th>\n",
|
| 427 |
+
" <th>NUT.CNT</th>\n",
|
| 428 |
+
" <th>PL.CNT</th>\n",
|
| 429 |
+
" <th>LYMP.CNT</th>\n",
|
| 430 |
+
" </tr>\n",
|
| 431 |
+
" </thead>\n",
|
| 432 |
+
" <tbody>\n",
|
| 433 |
+
" <tr>\n",
|
| 434 |
+
" <th>0</th>\n",
|
| 435 |
+
" <td>1</td>\n",
|
| 436 |
+
" <td>Suburban</td>\n",
|
| 437 |
+
" <td>IntactMale</td>\n",
|
| 438 |
+
" <td>0</td>\n",
|
| 439 |
+
" <td>9</td>\n",
|
| 440 |
+
" <td>0</td>\n",
|
| 441 |
+
" <td>6.4</td>\n",
|
| 442 |
+
" <td>16.6</td>\n",
|
| 443 |
+
" <td>14.2</td>\n",
|
| 444 |
+
" <td>142.0</td>\n",
|
| 445 |
+
" <td>852.0</td>\n",
|
| 446 |
+
" <td>6390.0</td>\n",
|
| 447 |
+
" <td>210.0</td>\n",
|
| 448 |
+
" <td>6816.0</td>\n",
|
| 449 |
+
" </tr>\n",
|
| 450 |
+
" <tr>\n",
|
| 451 |
+
" <th>1</th>\n",
|
| 452 |
+
" <td>0</td>\n",
|
| 453 |
+
" <td>Rural</td>\n",
|
| 454 |
+
" <td>NeuteredFemale</td>\n",
|
| 455 |
+
" <td>1</td>\n",
|
| 456 |
+
" <td>6</td>\n",
|
| 457 |
+
" <td>0</td>\n",
|
| 458 |
+
" <td>4.8</td>\n",
|
| 459 |
+
" <td>12.5</td>\n",
|
| 460 |
+
" <td>10.0</td>\n",
|
| 461 |
+
" <td>400.0</td>\n",
|
| 462 |
+
" <td>300.0</td>\n",
|
| 463 |
+
" <td>4800.0</td>\n",
|
| 464 |
+
" <td>209.0</td>\n",
|
| 465 |
+
" <td>4500.0</td>\n",
|
| 466 |
+
" </tr>\n",
|
| 467 |
+
" <tr>\n",
|
| 468 |
+
" <th>2</th>\n",
|
| 469 |
+
" <td>1</td>\n",
|
| 470 |
+
" <td>Suburban</td>\n",
|
| 471 |
+
" <td>IntactMale</td>\n",
|
| 472 |
+
" <td>0</td>\n",
|
| 473 |
+
" <td>14</td>\n",
|
| 474 |
+
" <td>0</td>\n",
|
| 475 |
+
" <td>6.2</td>\n",
|
| 476 |
+
" <td>17.3</td>\n",
|
| 477 |
+
" <td>9.5</td>\n",
|
| 478 |
+
" <td>190.0</td>\n",
|
| 479 |
+
" <td>475.0</td>\n",
|
| 480 |
+
" <td>7315.0</td>\n",
|
| 481 |
+
" <td>164.0</td>\n",
|
| 482 |
+
" <td>1520.0</td>\n",
|
| 483 |
+
" </tr>\n",
|
| 484 |
+
" <tr>\n",
|
| 485 |
+
" <th>3</th>\n",
|
| 486 |
+
" <td>1</td>\n",
|
| 487 |
+
" <td>Rural</td>\n",
|
| 488 |
+
" <td>IntactMale</td>\n",
|
| 489 |
+
" <td>0</td>\n",
|
| 490 |
+
" <td>6</td>\n",
|
| 491 |
+
" <td>0</td>\n",
|
| 492 |
+
" <td>5.4</td>\n",
|
| 493 |
+
" <td>13.8</td>\n",
|
| 494 |
+
" <td>14.1</td>\n",
|
| 495 |
+
" <td>1692.0</td>\n",
|
| 496 |
+
" <td>423.0</td>\n",
|
| 497 |
+
" <td>7755.0</td>\n",
|
| 498 |
+
" <td>254.0</td>\n",
|
| 499 |
+
" <td>4230.0</td>\n",
|
| 500 |
+
" </tr>\n",
|
| 501 |
+
" <tr>\n",
|
| 502 |
+
" <th>4</th>\n",
|
| 503 |
+
" <td>0</td>\n",
|
| 504 |
+
" <td>Rural</td>\n",
|
| 505 |
+
" <td>IntactFemale</td>\n",
|
| 506 |
+
" <td>0</td>\n",
|
| 507 |
+
" <td>18</td>\n",
|
| 508 |
+
" <td>0</td>\n",
|
| 509 |
+
" <td>5.9</td>\n",
|
| 510 |
+
" <td>14.4</td>\n",
|
| 511 |
+
" <td>6.5</td>\n",
|
| 512 |
+
" <td>390.0</td>\n",
|
| 513 |
+
" <td>130.0</td>\n",
|
| 514 |
+
" <td>2795.0</td>\n",
|
| 515 |
+
" <td>213.0</td>\n",
|
| 516 |
+
" <td>3185.0</td>\n",
|
| 517 |
+
" </tr>\n",
|
| 518 |
+
" </tbody>\n",
|
| 519 |
+
"</table>\n",
|
| 520 |
+
"</div>"
|
| 521 |
+
],
|
| 522 |
+
"text/plain": [
|
| 523 |
+
" SEX TYPEAREA SEX.REPRO REPRO.STATUS AGE PARASITE_STATUS RBC \\\n",
|
| 524 |
+
"0 1 Suburban IntactMale 0 9 0 6.4 \n",
|
| 525 |
+
"1 0 Rural NeuteredFemale 1 6 0 4.8 \n",
|
| 526 |
+
"2 1 Suburban IntactMale 0 14 0 6.2 \n",
|
| 527 |
+
"3 1 Rural IntactMale 0 6 0 5.4 \n",
|
| 528 |
+
"4 0 Rural IntactFemale 0 18 0 5.9 \n",
|
| 529 |
+
"\n",
|
| 530 |
+
" HGB WBC EOS.CNT MONO.CNT NUT.CNT PL.CNT LYMP.CNT \n",
|
| 531 |
+
"0 16.6 14.2 142.0 852.0 6390.0 210.0 6816.0 \n",
|
| 532 |
+
"1 12.5 10.0 400.0 300.0 4800.0 209.0 4500.0 \n",
|
| 533 |
+
"2 17.3 9.5 190.0 475.0 7315.0 164.0 1520.0 \n",
|
| 534 |
+
"3 13.8 14.1 1692.0 423.0 7755.0 254.0 4230.0 \n",
|
| 535 |
+
"4 14.4 6.5 390.0 130.0 2795.0 213.0 3185.0 "
|
| 536 |
+
]
|
| 537 |
+
},
|
| 538 |
+
"execution_count": 3,
|
| 539 |
+
"metadata": {},
|
| 540 |
+
"output_type": "execute_result"
|
| 541 |
+
}
|
| 542 |
+
],
|
| 543 |
+
"source": [
|
| 544 |
+
"from sklearn.preprocessing import LabelEncoder\n",
|
| 545 |
+
"le = LabelEncoder()\n",
|
| 546 |
+
"df2 = df.loc[:, df.columns != 'ID']\n",
|
| 547 |
+
"for x in ['SEX', 'REPRO.STATUS', 'PARASITE_STATUS']:\n",
|
| 548 |
+
" df2[x] = LabelEncoder().fit_transform(df2[x])\n",
|
| 549 |
+
"df2.head()"
|
| 550 |
+
]
|
| 551 |
+
},
|
| 552 |
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{
|
| 553 |
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"cell_type": "code",
|
| 554 |
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"execution_count": 4,
|
| 555 |
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"metadata": {
|
| 556 |
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"tags": []
|
| 557 |
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},
|
| 558 |
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"outputs": [
|
| 559 |
+
{
|
| 560 |
+
"data": {
|
| 561 |
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"text/plain": [
|
| 562 |
+
"Index(['SEX', 'REPRO.STATUS', 'AGE', 'PARASITE_STATUS', 'TYPEAREA_Rural',\n",
|
| 563 |
+
" 'TYPEAREA_Suburban', 'TYPEAREA_Urban', 'SEX.REPRO_IntactFemale',\n",
|
| 564 |
+
" 'SEX.REPRO_IntactMale', 'SEX.REPRO_NeuteredFemale',\n",
|
| 565 |
+
" 'SEX.REPRO_NeuteredMale'],\n",
|
| 566 |
+
" dtype='object')"
|
| 567 |
+
]
|
| 568 |
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},
|
| 569 |
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"execution_count": 4,
|
| 570 |
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"metadata": {},
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| 571 |
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"output_type": "execute_result"
|
| 572 |
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}
|
| 573 |
+
],
|
| 574 |
+
"source": [
|
| 575 |
+
"df3 = pd.get_dummies(df2).dropna(how='any', axis=1)\n",
|
| 576 |
+
"df3.columns"
|
| 577 |
+
]
|
| 578 |
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},
|
| 579 |
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{
|
| 580 |
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"cell_type": "code",
|
| 581 |
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"execution_count": 5,
|
| 582 |
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"metadata": {
|
| 583 |
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"tags": []
|
| 584 |
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},
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| 585 |
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|
| 586 |
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{
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| 587 |
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"data": {
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| 588 |
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|
| 605 |
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|
| 606 |
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" <th></th>\n",
|
| 607 |
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" <th>SEX</th>\n",
|
| 608 |
+
" <th>REPRO.STATUS</th>\n",
|
| 609 |
+
" <th>AGE</th>\n",
|
| 610 |
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" <th>PARASITE_STATUS</th>\n",
|
| 611 |
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|
| 612 |
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|
| 613 |
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|
| 614 |
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|
| 615 |
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|
| 616 |
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" <th>SEX.REPRO_NeuteredFemale</th>\n",
|
| 617 |
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" <th>SEX.REPRO_NeuteredMale</th>\n",
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| 618 |
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|
| 650 |
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| 651 |
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| 652 |
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| 653 |
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| 654 |
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|
| 655 |
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| 656 |
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| 657 |
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|
| 664 |
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|
| 665 |
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|
| 666 |
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| 675 |
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|
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|
| 677 |
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|
| 678 |
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|
| 679 |
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" <td>0</td>\n",
|
| 680 |
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" <td>0</td>\n",
|
| 681 |
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|
| 682 |
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|
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| 689 |
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|
| 690 |
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|
| 691 |
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|
| 692 |
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|
| 693 |
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"</div>"
|
| 694 |
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],
|
| 695 |
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"text/plain": [
|
| 696 |
+
" SEX REPRO.STATUS AGE PARASITE_STATUS TYPEAREA_Rural TYPEAREA_Suburban \\\n",
|
| 697 |
+
"0 1 0 9 0 0 1 \n",
|
| 698 |
+
"1 0 1 6 0 1 0 \n",
|
| 699 |
+
"2 1 0 14 0 0 1 \n",
|
| 700 |
+
"3 1 0 6 0 1 0 \n",
|
| 701 |
+
"4 0 0 18 0 1 0 \n",
|
| 702 |
+
"\n",
|
| 703 |
+
" TYPEAREA_Urban SEX.REPRO_IntactFemale SEX.REPRO_IntactMale \\\n",
|
| 704 |
+
"0 0 0 1 \n",
|
| 705 |
+
"1 0 0 0 \n",
|
| 706 |
+
"2 0 0 1 \n",
|
| 707 |
+
"3 0 0 1 \n",
|
| 708 |
+
"4 0 1 0 \n",
|
| 709 |
+
"\n",
|
| 710 |
+
" SEX.REPRO_NeuteredFemale SEX.REPRO_NeuteredMale \n",
|
| 711 |
+
"0 0 0 \n",
|
| 712 |
+
"1 1 0 \n",
|
| 713 |
+
"2 0 0 \n",
|
| 714 |
+
"3 0 0 \n",
|
| 715 |
+
"4 0 0 "
|
| 716 |
+
]
|
| 717 |
+
},
|
| 718 |
+
"execution_count": 5,
|
| 719 |
+
"metadata": {},
|
| 720 |
+
"output_type": "execute_result"
|
| 721 |
+
}
|
| 722 |
+
],
|
| 723 |
+
"source": [
|
| 724 |
+
"df3.head()"
|
| 725 |
+
]
|
| 726 |
+
},
|
| 727 |
+
{
|
| 728 |
+
"cell_type": "code",
|
| 729 |
+
"execution_count": 6,
|
| 730 |
+
"metadata": {
|
| 731 |
+
"tags": []
|
| 732 |
+
},
|
| 733 |
+
"outputs": [],
|
| 734 |
+
"source": [
|
| 735 |
+
"from sklearn.linear_model import LogisticRegression\n",
|
| 736 |
+
"from sklearn.model_selection import train_test_split\n",
|
| 737 |
+
"X = df3.drop(['PARASITE_STATUS'], axis=1)\n",
|
| 738 |
+
"y = df3['PARASITE_STATUS']\n",
|
| 739 |
+
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
|
| 740 |
+
]
|
| 741 |
+
},
|
| 742 |
+
{
|
| 743 |
+
"cell_type": "code",
|
| 744 |
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"execution_count": 19,
|
| 745 |
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"metadata": {
|
| 746 |
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"tags": []
|
| 747 |
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},
|
| 748 |
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|
| 749 |
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{
|
| 750 |
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"data": {
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| 751 |
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| 752 |
+
"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[('preprocessor', StandardScaler()),\n",
|
| 753 |
+
" ('classifier',\n",
|
| 754 |
+
" LogisticRegression(class_weight='balanced', max_iter=20000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocessor', StandardScaler()),\n",
|
| 755 |
+
" ('classifier',\n",
|
| 756 |
+
" LogisticRegression(class_weight='balanced', max_iter=20000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(class_weight='balanced', max_iter=20000)</pre></div></div></div></div></div></div></div>"
|
| 757 |
+
],
|
| 758 |
+
"text/plain": [
|
| 759 |
+
"Pipeline(steps=[('preprocessor', StandardScaler()),\n",
|
| 760 |
+
" ('classifier',\n",
|
| 761 |
+
" LogisticRegression(class_weight='balanced', max_iter=20000))])"
|
| 762 |
+
]
|
| 763 |
+
},
|
| 764 |
+
"execution_count": 19,
|
| 765 |
+
"metadata": {},
|
| 766 |
+
"output_type": "execute_result"
|
| 767 |
+
}
|
| 768 |
+
],
|
| 769 |
+
"source": [
|
| 770 |
+
"from sklearn.pipeline import Pipeline #make_pipeline\n",
|
| 771 |
+
"from sklearn import preprocessing\n",
|
| 772 |
+
"\n",
|
| 773 |
+
"i = 20000\n",
|
| 774 |
+
"\n",
|
| 775 |
+
"pipe = Pipeline([\n",
|
| 776 |
+
" ('preprocessor', preprocessing.StandardScaler()),\n",
|
| 777 |
+
" ('classifier', LogisticRegression(max_iter=i, class_weight='balanced')),\n",
|
| 778 |
+
"])\n",
|
| 779 |
+
"\n",
|
| 780 |
+
"#model = LogisticRegression(penalty='l1', solver='saga', max_iter=i)\n",
|
| 781 |
+
"pipe.fit(X_train, y_train)"
|
| 782 |
+
]
|
| 783 |
+
},
|
| 784 |
+
{
|
| 785 |
+
"cell_type": "code",
|
| 786 |
+
"execution_count": 32,
|
| 787 |
+
"metadata": {
|
| 788 |
+
"scrolled": true,
|
| 789 |
+
"tags": []
|
| 790 |
+
},
|
| 791 |
+
"outputs": [
|
| 792 |
+
{
|
| 793 |
+
"name": "stderr",
|
| 794 |
+
"output_type": "stream",
|
| 795 |
+
"text": [
|
| 796 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 797 |
+
" warnings.warn(\n",
|
| 798 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 799 |
+
" warnings.warn(\n",
|
| 800 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 801 |
+
" warnings.warn(\n",
|
| 802 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 803 |
+
" warnings.warn(\n",
|
| 804 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 805 |
+
" warnings.warn(\n",
|
| 806 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 807 |
+
" warnings.warn(\n",
|
| 808 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 809 |
+
" warnings.warn(\n",
|
| 810 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 811 |
+
" warnings.warn(\n",
|
| 812 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 813 |
+
" warnings.warn(\n",
|
| 814 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 815 |
+
" warnings.warn(\n",
|
| 816 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 817 |
+
" warnings.warn(\n",
|
| 818 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 819 |
+
" warnings.warn(\n",
|
| 820 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 821 |
+
" warnings.warn(\n",
|
| 822 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 823 |
+
" warnings.warn(\n",
|
| 824 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 825 |
+
" warnings.warn(\n",
|
| 826 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 827 |
+
" warnings.warn(\n",
|
| 828 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 829 |
+
" warnings.warn(\n",
|
| 830 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 831 |
+
" warnings.warn(\n",
|
| 832 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 833 |
+
" warnings.warn(\n",
|
| 834 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 835 |
+
" warnings.warn(\n",
|
| 836 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 837 |
+
" warnings.warn(\n",
|
| 838 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 839 |
+
" warnings.warn(\n",
|
| 840 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 841 |
+
" warnings.warn(\n",
|
| 842 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 843 |
+
" warnings.warn(\n",
|
| 844 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 845 |
+
" warnings.warn(\n",
|
| 846 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 847 |
+
" warnings.warn(\n",
|
| 848 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 849 |
+
" warnings.warn(\n",
|
| 850 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 851 |
+
" warnings.warn(\n",
|
| 852 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 853 |
+
" warnings.warn(\n",
|
| 854 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 855 |
+
" warnings.warn(\n",
|
| 856 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 857 |
+
" warnings.warn(\n",
|
| 858 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 859 |
+
" warnings.warn(\n",
|
| 860 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 861 |
+
" warnings.warn(\n",
|
| 862 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 863 |
+
" warnings.warn(\n",
|
| 864 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 865 |
+
" warnings.warn(\n",
|
| 866 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 867 |
+
" warnings.warn(\n",
|
| 868 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 869 |
+
" warnings.warn(\n",
|
| 870 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 871 |
+
" warnings.warn(\n",
|
| 872 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 873 |
+
" warnings.warn(\n",
|
| 874 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 875 |
+
" warnings.warn(\n",
|
| 876 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 877 |
+
" warnings.warn(\n",
|
| 878 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 879 |
+
" warnings.warn(\n",
|
| 880 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 881 |
+
" warnings.warn(\n",
|
| 882 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 883 |
+
" warnings.warn(\n",
|
| 884 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 885 |
+
" warnings.warn(\n",
|
| 886 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 887 |
+
" warnings.warn(\n",
|
| 888 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 889 |
+
" warnings.warn(\n",
|
| 890 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 891 |
+
" warnings.warn(\n",
|
| 892 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 893 |
+
" warnings.warn(\n",
|
| 894 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 895 |
+
" warnings.warn(\n",
|
| 896 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 897 |
+
" warnings.warn(\n",
|
| 898 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 899 |
+
" warnings.warn(\n",
|
| 900 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 901 |
+
" warnings.warn(\n",
|
| 902 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 903 |
+
" warnings.warn(\n",
|
| 904 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
|
| 905 |
+
" warnings.warn(\n",
|
| 906 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
|
| 907 |
+
"360 fits failed out of a total of 720.\n",
|
| 908 |
+
"The score on these train-test partitions for these parameters will be set to nan.\n",
|
| 909 |
+
"If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
|
| 910 |
+
"\n",
|
| 911 |
+
"Below are more details about the failures:\n",
|
| 912 |
+
"--------------------------------------------------------------------------------\n",
|
| 913 |
+
"360 fits failed with the following error:\n",
|
| 914 |
+
"Traceback (most recent call last):\n",
|
| 915 |
+
" File \"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
|
| 916 |
+
" estimator.fit(X_train, y_train, **fit_params)\n",
|
| 917 |
+
" File \"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/pipeline.py\", line 405, in fit\n",
|
| 918 |
+
" self._final_estimator.fit(Xt, y, **fit_params_last_step)\n",
|
| 919 |
+
" File \"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 1162, in fit\n",
|
| 920 |
+
" solver = _check_solver(self.solver, self.penalty, self.dual)\n",
|
| 921 |
+
" File \"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py\", line 54, in _check_solver\n",
|
| 922 |
+
" raise ValueError(\n",
|
| 923 |
+
"ValueError: Solver sag supports only 'l2' or 'none' penalties, got l1 penalty.\n",
|
| 924 |
+
"\n",
|
| 925 |
+
" warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
|
| 926 |
+
"/home/vagrant/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [ nan 0. nan 0.1590493 nan 0.\n",
|
| 927 |
+
" nan 0. nan 0. nan 0.\n",
|
| 928 |
+
" nan 0. nan 0. nan 0.\n",
|
| 929 |
+
" nan 0.13518741 nan 0.13518741 nan 0.13518741\n",
|
| 930 |
+
" nan 0. nan 0.16160635 nan 0.\n",
|
| 931 |
+
" nan 0. nan 0. nan 0.\n",
|
| 932 |
+
" nan 0. nan 0. nan 0.18677466\n",
|
| 933 |
+
" nan 0.13518741 nan 0.13518741 nan 0.13518741\n",
|
| 934 |
+
" nan 0. nan 0.16000688 nan 0.\n",
|
| 935 |
+
" nan 0. nan 0. nan 0.\n",
|
| 936 |
+
" nan 0. nan 0. nan 0.1923046\n",
|
| 937 |
+
" nan 0.13081038 nan 0.13518741 nan 0.13518741\n",
|
| 938 |
+
" nan 0. nan 0.15892573 nan 0.\n",
|
| 939 |
+
" nan 0. nan 0. nan 0.\n",
|
| 940 |
+
" nan 0. nan 0. nan 0.1887408\n",
|
| 941 |
+
" nan 0.13485348 nan 0.13518741 nan 0.13518741\n",
|
| 942 |
+
" nan 0. nan 0.12777313 nan 0.\n",
|
| 943 |
+
" nan 0. nan 0. nan 0.\n",
|
| 944 |
+
" nan 0. nan 0. nan 0.15035704\n",
|
| 945 |
+
" nan 0.13619257 nan 0.13642988 nan 0.13518741\n",
|
| 946 |
+
" nan 0. nan 0.12412204 nan 0.\n",
|
| 947 |
+
" nan 0. nan 0. nan 0.\n",
|
| 948 |
+
" nan 0. nan 0. nan 0.11476747\n",
|
| 949 |
+
" nan 0.14715202 nan 0.13721419 nan 0.13550537]\n",
|
| 950 |
+
" warnings.warn(\n"
|
| 951 |
+
]
|
| 952 |
+
},
|
| 953 |
+
{
|
| 954 |
+
"data": {
|
| 955 |
+
"text/plain": [
|
| 956 |
+
"{'classifier__C': 0.01,\n",
|
| 957 |
+
" 'classifier__class_weight': {0: 1, 1: 10},\n",
|
| 958 |
+
" 'classifier__penalty': 'l2',\n",
|
| 959 |
+
" 'classifier__solver': 'sag'}"
|
| 960 |
+
]
|
| 961 |
+
},
|
| 962 |
+
"execution_count": 32,
|
| 963 |
+
"metadata": {},
|
| 964 |
+
"output_type": "execute_result"
|
| 965 |
+
}
|
| 966 |
+
],
|
| 967 |
+
"source": [
|
| 968 |
+
"from sklearn.model_selection import GridSearchCV\n",
|
| 969 |
+
"import numpy as np\n",
|
| 970 |
+
"\n",
|
| 971 |
+
"\n",
|
| 972 |
+
"param_grid = {\n",
|
| 973 |
+
" 'classifier__penalty': ['l1', 'l2'],\n",
|
| 974 |
+
" 'classifier__C': [1e-4, 1e-3, 1e-2, 0.1, 1, 10],\n",
|
| 975 |
+
" 'classifier__solver': ['sag'],\n",
|
| 976 |
+
" 'classifier__class_weight': [None, 'balanced', *[{0: 1, 1:10**x} for x in range(-5, 5)]],\n",
|
| 977 |
+
" #'classifier__eta0': [10**x for x in range(-5, 5)],\n",
|
| 978 |
+
" #'classifier__batch_size': np.linspace(1, X_train.shape[0], 10, dtype=int),\n",
|
| 979 |
+
"}\n",
|
| 980 |
+
"\n",
|
| 981 |
+
"#grid_search = GridSearchCV(pipe, param_grid, cv=5, scoring='roc_auc')\n",
|
| 982 |
+
"grid_search = GridSearchCV(pipe, param_grid, cv=5, scoring='f1', n_jobs=-1)\n",
|
| 983 |
+
"\n",
|
| 984 |
+
"grid_search.fit(X_train, y_train)\n",
|
| 985 |
+
"grid_search.best_params_"
|
| 986 |
+
]
|
| 987 |
+
},
|
| 988 |
+
{
|
| 989 |
+
"cell_type": "code",
|
| 990 |
+
"execution_count": 35,
|
| 991 |
+
"metadata": {
|
| 992 |
+
"tags": []
|
| 993 |
+
},
|
| 994 |
+
"outputs": [
|
| 995 |
+
{
|
| 996 |
+
"data": {
|
| 997 |
+
"text/plain": [
|
| 998 |
+
"0.05732484076433121"
|
| 999 |
+
]
|
| 1000 |
+
},
|
| 1001 |
+
"execution_count": 35,
|
| 1002 |
+
"metadata": {},
|
| 1003 |
+
"output_type": "execute_result"
|
| 1004 |
+
}
|
| 1005 |
+
],
|
| 1006 |
+
"source": [
|
| 1007 |
+
"from sklearn.metrics import precision_score, recall_score, f1_score\n",
|
| 1008 |
+
"from sklearn.metrics import confusion_matrix\n",
|
| 1009 |
+
"\n",
|
| 1010 |
+
"y_pred = grid_search.predict(X_test)\n",
|
| 1011 |
+
"precision = precision_score(y_test, y_pred)\n",
|
| 1012 |
+
"precision"
|
| 1013 |
+
]
|
| 1014 |
+
},
|
| 1015 |
+
{
|
| 1016 |
+
"cell_type": "code",
|
| 1017 |
+
"execution_count": 36,
|
| 1018 |
+
"metadata": {
|
| 1019 |
+
"tags": []
|
| 1020 |
+
},
|
| 1021 |
+
"outputs": [
|
| 1022 |
+
{
|
| 1023 |
+
"data": {
|
| 1024 |
+
"text/plain": [
|
| 1025 |
+
"array([[ 27, 148],\n",
|
| 1026 |
+
" [420, 9]])"
|
| 1027 |
+
]
|
| 1028 |
+
},
|
| 1029 |
+
"execution_count": 36,
|
| 1030 |
+
"metadata": {},
|
| 1031 |
+
"output_type": "execute_result"
|
| 1032 |
+
}
|
| 1033 |
+
],
|
| 1034 |
+
"source": [
|
| 1035 |
+
"confusion_matrix(y_pred == y_test, y_pred)"
|
| 1036 |
+
]
|
| 1037 |
+
},
|
| 1038 |
+
{
|
| 1039 |
+
"cell_type": "code",
|
| 1040 |
+
"execution_count": 11,
|
| 1041 |
+
"metadata": {
|
| 1042 |
+
"tags": []
|
| 1043 |
+
},
|
| 1044 |
+
"outputs": [
|
| 1045 |
+
{
|
| 1046 |
+
"data": {
|
| 1047 |
+
"text/plain": [
|
| 1048 |
+
"\u001b[0;31mSignature:\u001b[0m\n",
|
| 1049 |
+
"\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
|
| 1050 |
+
"\u001b[0;34m\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
|
| 1051 |
+
"\u001b[0;34m\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
|
| 1052 |
+
"\u001b[0;34m\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
|
| 1053 |
+
"\u001b[0;34m\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
|
| 1054 |
+
"\u001b[0;34m\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
|
| 1055 |
+
"\u001b[0;34m\u001b[0m \u001b[0mnormalize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
|
| 1056 |
+
"\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
| 1057 |
+
"\u001b[0;31mDocstring:\u001b[0m\n",
|
| 1058 |
+
"Compute confusion matrix to evaluate the accuracy of a classification.\n",
|
| 1059 |
+
"\n",
|
| 1060 |
+
"By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`\n",
|
| 1061 |
+
"is equal to the number of observations known to be in group :math:`i` and\n",
|
| 1062 |
+
"predicted to be in group :math:`j`.\n",
|
| 1063 |
+
"\n",
|
| 1064 |
+
"Thus in binary classification, the count of true negatives is\n",
|
| 1065 |
+
":math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is\n",
|
| 1066 |
+
":math:`C_{1,1}` and false positives is :math:`C_{0,1}`.\n",
|
| 1067 |
+
"\n",
|
| 1068 |
+
"Read more in the :ref:`User Guide <confusion_matrix>`.\n",
|
| 1069 |
+
"\n",
|
| 1070 |
+
"Parameters\n",
|
| 1071 |
+
"----------\n",
|
| 1072 |
+
"y_true : array-like of shape (n_samples,)\n",
|
| 1073 |
+
" Ground truth (correct) target values.\n",
|
| 1074 |
+
"\n",
|
| 1075 |
+
"y_pred : array-like of shape (n_samples,)\n",
|
| 1076 |
+
" Estimated targets as returned by a classifier.\n",
|
| 1077 |
+
"\n",
|
| 1078 |
+
"labels : array-like of shape (n_classes), default=None\n",
|
| 1079 |
+
" List of labels to index the matrix. This may be used to reorder\n",
|
| 1080 |
+
" or select a subset of labels.\n",
|
| 1081 |
+
" If ``None`` is given, those that appear at least once\n",
|
| 1082 |
+
" in ``y_true`` or ``y_pred`` are used in sorted order.\n",
|
| 1083 |
+
"\n",
|
| 1084 |
+
"sample_weight : array-like of shape (n_samples,), default=None\n",
|
| 1085 |
+
" Sample weights.\n",
|
| 1086 |
+
"\n",
|
| 1087 |
+
" .. versionadded:: 0.18\n",
|
| 1088 |
+
"\n",
|
| 1089 |
+
"normalize : {'true', 'pred', 'all'}, default=None\n",
|
| 1090 |
+
" Normalizes confusion matrix over the true (rows), predicted (columns)\n",
|
| 1091 |
+
" conditions or all the population. If None, confusion matrix will not be\n",
|
| 1092 |
+
" normalized.\n",
|
| 1093 |
+
"\n",
|
| 1094 |
+
"Returns\n",
|
| 1095 |
+
"-------\n",
|
| 1096 |
+
"C : ndarray of shape (n_classes, n_classes)\n",
|
| 1097 |
+
" Confusion matrix whose i-th row and j-th\n",
|
| 1098 |
+
" column entry indicates the number of\n",
|
| 1099 |
+
" samples with true label being i-th class\n",
|
| 1100 |
+
" and predicted label being j-th class.\n",
|
| 1101 |
+
"\n",
|
| 1102 |
+
"See Also\n",
|
| 1103 |
+
"--------\n",
|
| 1104 |
+
"ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix\n",
|
| 1105 |
+
" given an estimator, the data, and the label.\n",
|
| 1106 |
+
"ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix\n",
|
| 1107 |
+
" given the true and predicted labels.\n",
|
| 1108 |
+
"ConfusionMatrixDisplay : Confusion Matrix visualization.\n",
|
| 1109 |
+
"\n",
|
| 1110 |
+
"References\n",
|
| 1111 |
+
"----------\n",
|
| 1112 |
+
".. [1] `Wikipedia entry for the Confusion matrix\n",
|
| 1113 |
+
" <https://en.wikipedia.org/wiki/Confusion_matrix>`_\n",
|
| 1114 |
+
" (Wikipedia and other references may use a different\n",
|
| 1115 |
+
" convention for axes).\n",
|
| 1116 |
+
"\n",
|
| 1117 |
+
"Examples\n",
|
| 1118 |
+
"--------\n",
|
| 1119 |
+
">>> from sklearn.metrics import confusion_matrix\n",
|
| 1120 |
+
">>> y_true = [2, 0, 2, 2, 0, 1]\n",
|
| 1121 |
+
">>> y_pred = [0, 0, 2, 2, 0, 2]\n",
|
| 1122 |
+
">>> confusion_matrix(y_true, y_pred)\n",
|
| 1123 |
+
"array([[2, 0, 0],\n",
|
| 1124 |
+
" [0, 0, 1],\n",
|
| 1125 |
+
" [1, 0, 2]])\n",
|
| 1126 |
+
"\n",
|
| 1127 |
+
">>> y_true = [\"cat\", \"ant\", \"cat\", \"cat\", \"ant\", \"bird\"]\n",
|
| 1128 |
+
">>> y_pred = [\"ant\", \"ant\", \"cat\", \"cat\", \"ant\", \"cat\"]\n",
|
| 1129 |
+
">>> confusion_matrix(y_true, y_pred, labels=[\"ant\", \"bird\", \"cat\"])\n",
|
| 1130 |
+
"array([[2, 0, 0],\n",
|
| 1131 |
+
" [0, 0, 1],\n",
|
| 1132 |
+
" [1, 0, 2]])\n",
|
| 1133 |
+
"\n",
|
| 1134 |
+
"In the binary case, we can extract true positives, etc as follows:\n",
|
| 1135 |
+
"\n",
|
| 1136 |
+
">>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()\n",
|
| 1137 |
+
">>> (tn, fp, fn, tp)\n",
|
| 1138 |
+
"(0, 2, 1, 1)\n",
|
| 1139 |
+
"\u001b[0;31mFile:\u001b[0m ~/.local/share/virtualenvs/midterm-5qaZhyTt/lib/python3.10/site-packages/sklearn/metrics/_classification.py\n",
|
| 1140 |
+
"\u001b[0;31mType:\u001b[0m function"
|
| 1141 |
+
]
|
| 1142 |
+
},
|
| 1143 |
+
"metadata": {},
|
| 1144 |
+
"output_type": "display_data"
|
| 1145 |
+
}
|
| 1146 |
+
],
|
| 1147 |
+
"source": [
|
| 1148 |
+
"confusion_matrix?"
|
| 1149 |
+
]
|
| 1150 |
+
},
|
| 1151 |
+
{
|
| 1152 |
+
"cell_type": "code",
|
| 1153 |
+
"execution_count": 56,
|
| 1154 |
+
"metadata": {
|
| 1155 |
+
"tags": []
|
| 1156 |
+
},
|
| 1157 |
+
"outputs": [
|
| 1158 |
+
{
|
| 1159 |
+
"data": {
|
| 1160 |
+
"text/plain": [
|
| 1161 |
+
"array([0.93046358, 0.93046358, 0.92880795, 0.93034826, 0.93034826])"
|
| 1162 |
+
]
|
| 1163 |
+
},
|
| 1164 |
+
"execution_count": 56,
|
| 1165 |
+
"metadata": {},
|
| 1166 |
+
"output_type": "execute_result"
|
| 1167 |
+
}
|
| 1168 |
+
],
|
| 1169 |
+
"source": [
|
| 1170 |
+
"from sklearn.model_selection import cross_val_score\n",
|
| 1171 |
+
"\n",
|
| 1172 |
+
"scores = cross_val_score(pipe, X, y, cv=5)\n",
|
| 1173 |
+
"scores"
|
| 1174 |
+
]
|
| 1175 |
+
},
|
| 1176 |
+
{
|
| 1177 |
+
"cell_type": "code",
|
| 1178 |
+
"execution_count": 12,
|
| 1179 |
+
"metadata": {
|
| 1180 |
+
"tags": []
|
| 1181 |
+
},
|
| 1182 |
+
"outputs": [
|
| 1183 |
+
{
|
| 1184 |
+
"data": {
|
| 1185 |
+
"text/plain": [
|
| 1186 |
+
"{'memory': None,\n",
|
| 1187 |
+
" 'steps': [('preprocessor', StandardScaler()),\n",
|
| 1188 |
+
" ('classifier', LogisticRegression(class_weight='balanced', max_iter=200))],\n",
|
| 1189 |
+
" 'verbose': False,\n",
|
| 1190 |
+
" 'preprocessor': StandardScaler(),\n",
|
| 1191 |
+
" 'classifier': LogisticRegression(class_weight='balanced', max_iter=200),\n",
|
| 1192 |
+
" 'preprocessor__copy': True,\n",
|
| 1193 |
+
" 'preprocessor__with_mean': True,\n",
|
| 1194 |
+
" 'preprocessor__with_std': True,\n",
|
| 1195 |
+
" 'classifier__C': 1.0,\n",
|
| 1196 |
+
" 'classifier__class_weight': 'balanced',\n",
|
| 1197 |
+
" 'classifier__dual': False,\n",
|
| 1198 |
+
" 'classifier__fit_intercept': True,\n",
|
| 1199 |
+
" 'classifier__intercept_scaling': 1,\n",
|
| 1200 |
+
" 'classifier__l1_ratio': None,\n",
|
| 1201 |
+
" 'classifier__max_iter': 200,\n",
|
| 1202 |
+
" 'classifier__multi_class': 'auto',\n",
|
| 1203 |
+
" 'classifier__n_jobs': None,\n",
|
| 1204 |
+
" 'classifier__penalty': 'l2',\n",
|
| 1205 |
+
" 'classifier__random_state': None,\n",
|
| 1206 |
+
" 'classifier__solver': 'lbfgs',\n",
|
| 1207 |
+
" 'classifier__tol': 0.0001,\n",
|
| 1208 |
+
" 'classifier__verbose': 0,\n",
|
| 1209 |
+
" 'classifier__warm_start': False}"
|
| 1210 |
+
]
|
| 1211 |
+
},
|
| 1212 |
+
"execution_count": 12,
|
| 1213 |
+
"metadata": {},
|
| 1214 |
+
"output_type": "execute_result"
|
| 1215 |
+
}
|
| 1216 |
+
],
|
| 1217 |
+
"source": [
|
| 1218 |
+
"pipe.get_params()"
|
| 1219 |
+
]
|
| 1220 |
+
},
|
| 1221 |
+
{
|
| 1222 |
+
"cell_type": "code",
|
| 1223 |
+
"execution_count": null,
|
| 1224 |
+
"metadata": {},
|
| 1225 |
+
"outputs": [],
|
| 1226 |
+
"source": []
|
| 1227 |
+
}
|
| 1228 |
+
],
|
| 1229 |
+
"metadata": {
|
| 1230 |
+
"colab": {
|
| 1231 |
+
"provenance": []
|
| 1232 |
+
},
|
| 1233 |
+
"kernelspec": {
|
| 1234 |
+
"display_name": "Python 3 (ipykernel)",
|
| 1235 |
+
"language": "python",
|
| 1236 |
+
"name": "python3"
|
| 1237 |
+
},
|
| 1238 |
+
"language_info": {
|
| 1239 |
+
"codemirror_mode": {
|
| 1240 |
+
"name": "ipython",
|
| 1241 |
+
"version": 3
|
| 1242 |
+
},
|
| 1243 |
+
"file_extension": ".py",
|
| 1244 |
+
"mimetype": "text/x-python",
|
| 1245 |
+
"name": "python",
|
| 1246 |
+
"nbconvert_exporter": "python",
|
| 1247 |
+
"pygments_lexer": "ipython3",
|
| 1248 |
+
"version": "3.10.4"
|
| 1249 |
+
},
|
| 1250 |
+
"vscode": {
|
| 1251 |
+
"interpreter": {
|
| 1252 |
+
"hash": "62556f7a043365a66e0918c892755cfafede529a87e97207556f006a109bade4"
|
| 1253 |
+
}
|
| 1254 |
+
}
|
| 1255 |
+
},
|
| 1256 |
+
"nbformat": 4,
|
| 1257 |
+
"nbformat_minor": 4
|
| 1258 |
+
}
|
midterm/pyproject.toml
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[tool.poetry]
|
| 2 |
+
name = "midterm"
|
| 3 |
+
version = "0.1.0"
|
| 4 |
+
description = ""
|
| 5 |
+
authors = ["Your Name <[email protected]>"]
|
| 6 |
+
readme = "README.md"
|
| 7 |
+
|
| 8 |
+
[tool.poetry.dependencies]
|
| 9 |
+
python = "^3.10"
|
| 10 |
+
black = "^23.1.0"
|
| 11 |
+
jupyterlab = "^3.6.1"
|
| 12 |
+
ipython = "^8.10.0"
|
| 13 |
+
numpy = "^1.24.2"
|
| 14 |
+
pandas = "^1.5.3"
|
| 15 |
+
jax = "^0.4.4"
|
| 16 |
+
seaborn = "^0.12.2"
|
| 17 |
+
matplotlib = "^3.7.0"
|
| 18 |
+
|
| 19 |
+
|
| 20 |
+
[build-system]
|
| 21 |
+
requires = ["poetry-core"]
|
| 22 |
+
build-backend = "poetry.core.masonry.api"
|
midterm/tests/__init__.py
ADDED
|
File without changes
|