Upload 4 files

Added main files

Iris.csv

@@ -0,0 +1,151 @@
+Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species
+1,5.1,3.5,1.4,0.2,Iris-setosa
+2,4.9,3.0,1.4,0.2,Iris-setosa
+3,4.7,3.2,1.3,0.2,Iris-setosa
+4,4.6,3.1,1.5,0.2,Iris-setosa
+5,5.0,3.6,1.4,0.2,Iris-setosa
+6,5.4,3.9,1.7,0.4,Iris-setosa
+7,4.6,3.4,1.4,0.3,Iris-setosa
+8,5.0,3.4,1.5,0.2,Iris-setosa
+9,4.4,2.9,1.4,0.2,Iris-setosa
+10,4.9,3.1,1.5,0.1,Iris-setosa
+11,5.4,3.7,1.5,0.2,Iris-setosa
+12,4.8,3.4,1.6,0.2,Iris-setosa
+13,4.8,3.0,1.4,0.1,Iris-setosa
+14,4.3,3.0,1.1,0.1,Iris-setosa
+15,5.8,4.0,1.2,0.2,Iris-setosa
+16,5.7,4.4,1.5,0.4,Iris-setosa
+17,5.4,3.9,1.3,0.4,Iris-setosa
+18,5.1,3.5,1.4,0.3,Iris-setosa
+19,5.7,3.8,1.7,0.3,Iris-setosa
+20,5.1,3.8,1.5,0.3,Iris-setosa
+21,5.4,3.4,1.7,0.2,Iris-setosa
+22,5.1,3.7,1.5,0.4,Iris-setosa
+23,4.6,3.6,1.0,0.2,Iris-setosa
+24,5.1,3.3,1.7,0.5,Iris-setosa
+25,4.8,3.4,1.9,0.2,Iris-setosa
+26,5.0,3.0,1.6,0.2,Iris-setosa
+27,5.0,3.4,1.6,0.4,Iris-setosa
+28,5.2,3.5,1.5,0.2,Iris-setosa
+29,5.2,3.4,1.4,0.2,Iris-setosa
+30,4.7,3.2,1.6,0.2,Iris-setosa
+31,4.8,3.1,1.6,0.2,Iris-setosa
+32,5.4,3.4,1.5,0.4,Iris-setosa
+33,5.2,4.1,1.5,0.1,Iris-setosa
+34,5.5,4.2,1.4,0.2,Iris-setosa
+35,4.9,3.1,1.5,0.1,Iris-setosa
+36,5.0,3.2,1.2,0.2,Iris-setosa
+37,5.5,3.5,1.3,0.2,Iris-setosa
+38,4.9,3.1,1.5,0.1,Iris-setosa
+39,4.4,3.0,1.3,0.2,Iris-setosa
+40,5.1,3.4,1.5,0.2,Iris-setosa
+41,5.0,3.5,1.3,0.3,Iris-setosa
+42,4.5,2.3,1.3,0.3,Iris-setosa
+43,4.4,3.2,1.3,0.2,Iris-setosa
+44,5.0,3.5,1.6,0.6,Iris-setosa
+45,5.1,3.8,1.9,0.4,Iris-setosa
+46,4.8,3.0,1.4,0.3,Iris-setosa
+47,5.1,3.8,1.6,0.2,Iris-setosa
+48,4.6,3.2,1.4,0.2,Iris-setosa
+49,5.3,3.7,1.5,0.2,Iris-setosa
+50,5.0,3.3,1.4,0.2,Iris-setosa
+51,7.0,3.2,4.7,1.4,Iris-versicolor
+52,6.4,3.2,4.5,1.5,Iris-versicolor
+53,6.9,3.1,4.9,1.5,Iris-versicolor
+54,5.5,2.3,4.0,1.3,Iris-versicolor
+55,6.5,2.8,4.6,1.5,Iris-versicolor
+56,5.7,2.8,4.5,1.3,Iris-versicolor
+57,6.3,3.3,4.7,1.6,Iris-versicolor
+58,4.9,2.4,3.3,1.0,Iris-versicolor
+59,6.6,2.9,4.6,1.3,Iris-versicolor
+60,5.2,2.7,3.9,1.4,Iris-versicolor
+61,5.0,2.0,3.5,1.0,Iris-versicolor
+62,5.9,3.0,4.2,1.5,Iris-versicolor
+63,6.0,2.2,4.0,1.0,Iris-versicolor
+64,6.1,2.9,4.7,1.4,Iris-versicolor
+65,5.6,2.9,3.6,1.3,Iris-versicolor
+66,6.7,3.1,4.4,1.4,Iris-versicolor
+67,5.6,3.0,4.5,1.5,Iris-versicolor
+68,5.8,2.7,4.1,1.0,Iris-versicolor
+69,6.2,2.2,4.5,1.5,Iris-versicolor
+70,5.6,2.5,3.9,1.1,Iris-versicolor
+71,5.9,3.2,4.8,1.8,Iris-versicolor
+72,6.1,2.8,4.0,1.3,Iris-versicolor
+73,6.3,2.5,4.9,1.5,Iris-versicolor
+74,6.1,2.8,4.7,1.2,Iris-versicolor
+75,6.4,2.9,4.3,1.3,Iris-versicolor
+76,6.6,3.0,4.4,1.4,Iris-versicolor
+77,6.8,2.8,4.8,1.4,Iris-versicolor
+78,6.7,3.0,5.0,1.7,Iris-versicolor
+79,6.0,2.9,4.5,1.5,Iris-versicolor
+80,5.7,2.6,3.5,1.0,Iris-versicolor
+81,5.5,2.4,3.8,1.1,Iris-versicolor
+82,5.5,2.4,3.7,1.0,Iris-versicolor
+83,5.8,2.7,3.9,1.2,Iris-versicolor
+84,6.0,2.7,5.1,1.6,Iris-versicolor
+85,5.4,3.0,4.5,1.5,Iris-versicolor
+86,6.0,3.4,4.5,1.6,Iris-versicolor
+87,6.7,3.1,4.7,1.5,Iris-versicolor
+88,6.3,2.3,4.4,1.3,Iris-versicolor
+89,5.6,3.0,4.1,1.3,Iris-versicolor
+90,5.5,2.5,4.0,1.3,Iris-versicolor
+91,5.5,2.6,4.4,1.2,Iris-versicolor
+92,6.1,3.0,4.6,1.4,Iris-versicolor
+93,5.8,2.6,4.0,1.2,Iris-versicolor
+94,5.0,2.3,3.3,1.0,Iris-versicolor
+95,5.6,2.7,4.2,1.3,Iris-versicolor
+96,5.7,3.0,4.2,1.2,Iris-versicolor
+97,5.7,2.9,4.2,1.3,Iris-versicolor
+98,6.2,2.9,4.3,1.3,Iris-versicolor
+99,5.1,2.5,3.0,1.1,Iris-versicolor
+100,5.7,2.8,4.1,1.3,Iris-versicolor
+101,6.3,3.3,6.0,2.5,Iris-virginica
+102,5.8,2.7,5.1,1.9,Iris-virginica
+103,7.1,3.0,5.9,2.1,Iris-virginica
+104,6.3,2.9,5.6,1.8,Iris-virginica
+105,6.5,3.0,5.8,2.2,Iris-virginica
+106,7.6,3.0,6.6,2.1,Iris-virginica
+107,4.9,2.5,4.5,1.7,Iris-virginica
+108,7.3,2.9,6.3,1.8,Iris-virginica
+109,6.7,2.5,5.8,1.8,Iris-virginica
+110,7.2,3.6,6.1,2.5,Iris-virginica
+111,6.5,3.2,5.1,2.0,Iris-virginica
+112,6.4,2.7,5.3,1.9,Iris-virginica
+113,6.8,3.0,5.5,2.1,Iris-virginica
+114,5.7,2.5,5.0,2.0,Iris-virginica
+115,5.8,2.8,5.1,2.4,Iris-virginica
+116,6.4,3.2,5.3,2.3,Iris-virginica
+117,6.5,3.0,5.5,1.8,Iris-virginica
+118,7.7,3.8,6.7,2.2,Iris-virginica
+119,7.7,2.6,6.9,2.3,Iris-virginica
+120,6.0,2.2,5.0,1.5,Iris-virginica
+121,6.9,3.2,5.7,2.3,Iris-virginica
+122,5.6,2.8,4.9,2.0,Iris-virginica
+123,7.7,2.8,6.7,2.0,Iris-virginica
+124,6.3,2.7,4.9,1.8,Iris-virginica
+125,6.7,3.3,5.7,2.1,Iris-virginica
+126,7.2,3.2,6.0,1.8,Iris-virginica
+127,6.2,2.8,4.8,1.8,Iris-virginica
+128,6.1,3.0,4.9,1.8,Iris-virginica
+129,6.4,2.8,5.6,2.1,Iris-virginica
+130,7.2,3.0,5.8,1.6,Iris-virginica
+131,7.4,2.8,6.1,1.9,Iris-virginica
+132,7.9,3.8,6.4,2.0,Iris-virginica
+133,6.4,2.8,5.6,2.2,Iris-virginica
+134,6.3,2.8,5.1,1.5,Iris-virginica
+135,6.1,2.6,5.6,1.4,Iris-virginica
+136,7.7,3.0,6.1,2.3,Iris-virginica
+137,6.3,3.4,5.6,2.4,Iris-virginica
+138,6.4,3.1,5.5,1.8,Iris-virginica
+139,6.0,3.0,4.8,1.8,Iris-virginica
+140,6.9,3.1,5.4,2.1,Iris-virginica
+141,6.7,3.1,5.6,2.4,Iris-virginica
+142,6.9,3.1,5.1,2.3,Iris-virginica
+143,5.8,2.7,5.1,1.9,Iris-virginica
+144,6.8,3.2,5.9,2.3,Iris-virginica
+145,6.7,3.3,5.7,2.5,Iris-virginica
+146,6.7,3.0,5.2,2.3,Iris-virginica
+147,6.3,2.5,5.0,1.9,Iris-virginica
+148,6.5,3.0,5.2,2.0,Iris-virginica
+149,6.2,3.4,5.4,2.3,Iris-virginica
+150,5.9,3.0,5.1,1.8,Iris-virginica

README.md

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+# Internship appplication task
+Position: ML Open Source Engineer Internship - skops: Hugging Face and scikit-learn
+
+## Task requirments
+1. Create a python environment and install `scikit-learn` version `1.0` in that environment.
+2. Using that environment, create a `LogisticRegression` model and fit it on the Iris dataset.
+3. Save the trained model using `pickle` or `joblib`.
+4. Create a second environment, and install `scikit-learn` version `1.1` in it.
+5. Try loading the model you saved in step 3 in this second environment.  
+
+
+## Steps Taken
+1. I used mamba to create the environment locally.
+2. Trained a simple logistic regression model in `model.ipynb`.
+3. Used pickle to save the model in `finalized_model.sav`.
+4. Created another environment with `scikit-learn` version `1.1`.
+5. Imported the model in `importingmodel.ipynb`.
+
+## Observations
+A warning is shown when trying to load the model.
+```
+/home/tarek/.local/lib/python3.8/site-packages/sklearn/base.py:329: UserWarning: Trying to unpickle estimator LogisticRegression from version 1.0.2 when using version 1.1.0. This might lead to breaking code or invalid results. Use at your own risk. For more info please refer to:
+https://scikit-learn.org/stable/modules/model_persistence.html#security-maintainability-limitations
+  warnings.warn(
+```

model.ipynb

@@ -0,0 +1,379 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "14741086",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sklearn version: 1.0.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import sklearn\n",
+    "print(\"sklearn version: \" + sklearn.__version__)\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn import metrics\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "96b17451",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('Iris.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cefb0143",
+   "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>Id</th>\n",
+       "      <th>SepalLengthCm</th>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <th>Species</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>Iris-setosa</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
+       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
+       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
+       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
+       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
+       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f3c67f44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of dataset: (150, 6)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Shape of dataset: {df.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "60e037d4",
+   "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>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>min</th>\n",
+       "      <th>25%</th>\n",
+       "      <th>50%</th>\n",
+       "      <th>75%</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Id</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>75.500000</td>\n",
+       "      <td>43.445368</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>38.25</td>\n",
+       "      <td>75.50</td>\n",
+       "      <td>112.75</td>\n",
+       "      <td>150.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SepalLengthCm</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>5.843333</td>\n",
+       "      <td>0.828066</td>\n",
+       "      <td>4.3</td>\n",
+       "      <td>5.10</td>\n",
+       "      <td>5.80</td>\n",
+       "      <td>6.40</td>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SepalWidthCm</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3.054000</td>\n",
+       "      <td>0.433594</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.80</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>4.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PetalLengthCm</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3.758667</td>\n",
+       "      <td>1.764420</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>4.35</td>\n",
+       "      <td>5.10</td>\n",
+       "      <td>6.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PetalWidthCm</th>\n",
+       "      <td>150.0</td>\n",
+       "      <td>1.198667</td>\n",
+       "      <td>0.763161</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>2.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               count       mean        std  min    25%    50%     75%    max\n",
+       "Id             150.0  75.500000  43.445368  1.0  38.25  75.50  112.75  150.0\n",
+       "SepalLengthCm  150.0   5.843333   0.828066  4.3   5.10   5.80    6.40    7.9\n",
+       "SepalWidthCm   150.0   3.054000   0.433594  2.0   2.80   3.00    3.30    4.4\n",
+       "PetalLengthCm  150.0   3.758667   1.764420  1.0   1.60   4.35    5.10    6.9\n",
+       "PetalWidthCm   150.0   1.198667   0.763161  0.1   0.30   1.30    1.80    2.5"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe().T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "60f28e3c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 4)\n",
+      "(150,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = df.drop(['Id', 'Species'], axis=1)\n",
+    "y = df['Species']\n",
+    "# print(X.head())\n",
+    "print(X.shape)\n",
+    "# print(y.head())\n",
+    "print(y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d76a6b95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(90, 4)\n",
+      "(90,)\n",
+      "(60, 4)\n",
+      "(60,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=5)\n",
+    "print(X_train.shape)\n",
+    "print(y_train.shape)\n",
+    "print(X_test.shape)\n",
+    "print(y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b1da053e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9833333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "logreg = LogisticRegression()\n",
+    "logreg.fit(X_train, y_train)\n",
+    "y_pred = logreg.predict(X_test)\n",
+    "print(metrics.accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2d47b3df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = 'finalized_model.sav'\n",
+    "pickle.dump(logreg, open(filename, 'wb'))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}