From 772ad7445f166267023fc3ed417b4dbfefe1b384 Mon Sep 17 00:00:00 2001 From: Jen Looper Date: Thu, 6 May 2021 21:37:29 -0400 Subject: [PATCH] build an api --- API/1-API/README.md | 13 + API/1-API/assignment.md | 1 + API/1-API/solution/lin-reg-model.pkl | Bin 0 -> 528 bytes API/1-API/solution/notebook.ipynb | 275 ++++++++++++++++++++ API/1-API/translations/README.es.md | 0 API/README.md | 11 + API/translations/README.es.md | 0 Regression/3-Linear/solution/notebook.ipynb | 82 +++--- 8 files changed, 343 insertions(+), 39 deletions(-) create mode 100644 API/1-API/README.md create mode 100644 API/1-API/assignment.md create mode 100644 API/1-API/solution/lin-reg-model.pkl create mode 100644 API/1-API/solution/notebook.ipynb create mode 100644 API/1-API/translations/README.es.md create mode 100644 API/README.md create mode 100644 API/translations/README.es.md diff --git a/API/1-API/README.md b/API/1-API/README.md new file mode 100644 index 000000000..cbb786a09 --- /dev/null +++ b/API/1-API/README.md @@ -0,0 +1,13 @@ +# Build an API + +## [Pre-lecture quiz](link-to-quiz-app) + +✅ Knowledge Check - use this moment to stretch students' knowledge with open questions + +🚀 Challenge: Add a challenge for students to work on collaboratively in class to enhance the project + +## [Post-lecture quiz](link-to-quiz-app) + +## Review & Self Study + +**Assignment**: [Assignment Name](assignment.md) diff --git a/API/1-API/assignment.md b/API/1-API/assignment.md new file mode 100644 index 000000000..346ffdaef --- /dev/null +++ b/API/1-API/assignment.md @@ -0,0 +1 @@ +# Assignment \ No newline at end of file diff --git a/API/1-API/solution/lin-reg-model.pkl b/API/1-API/solution/lin-reg-model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..2ffbbf826c4431be0448c06bc5e2322b6ac4d511 GIT binary patch literal 528 zcmX|8%T5$Q6zyhs3@s=OPv3$f!@~{&CTd)aVP~6J^ukoaqSDnLI#>7Mx z$jp)*abe=hwL8B-mu&nA{ROQq2CI^~sdMi+_w}I^tAmtBQut{ijHRut!Bd|_ihHju z67qe?l}a+tx9K_#VYbEb>;n+O`k{`~%@^s7?CjK(apjhSjx(#W~OT(jJ?$>*CU^YsunZ02L!Y*q~S$LE({ z=D(fSa7*Jhvwg})98lZ|T&tuKv7e`A*0_7Abgxia2(j2#x^I={wzq!oK7Uff1C1p{ zJX7)ic~d;RBrg}_YKWCSdDW8NJo|KZy0u-yn#MXK6NXItyQYU+77N8AyORxn^Rd4P HKWF|w(ZHrx literal 0 HcmV?d00001 diff --git a/API/1-API/solution/notebook.ipynb b/API/1-API/solution/notebook.ipynb new file mode 100644 index 000000000..078e1693b --- /dev/null +++ b/API/1-API/solution/notebook.ipynb @@ -0,0 +1,275 @@ +{ + "metadata": { + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.0" + }, + "orig_nbformat": 2, + "kernelspec": { + "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7", + "display_name": "Python 3.7.0 64-bit ('3.7')" + }, + "metadata": { + "interpreter": { + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2, + "cells": [ + { + "source": [ + "## Build an API with two different models\n", + "\n", + "Linear Regression\n", + "Classification" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " City Name Type Package Variety Sub Variety Grade Date \\\n", + "0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n", + "1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n", + "2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", + "3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", + "4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n", + "\n", + " Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n", + "0 270.0 280.0 270.0 ... NaN NaN NaN \n", + "1 270.0 280.0 270.0 ... NaN NaN NaN \n", + "2 160.0 160.0 160.0 ... NaN NaN NaN \n", + "3 160.0 160.0 160.0 ... NaN NaN NaN \n", + "4 90.0 100.0 90.0 ... NaN NaN NaN \n", + "\n", + " Appearance Storage Crop Repack Trans Mode Unnamed: 24 Unnamed: 25 \n", + "0 NaN NaN NaN E NaN NaN NaN \n", + "1 NaN NaN NaN E NaN NaN NaN \n", + "2 NaN NaN NaN N NaN NaN NaN \n", + "3 NaN NaN NaN N NaN NaN NaN \n", + "4 NaN NaN NaN N NaN NaN NaN \n", + "\n", + "[5 rows x 26 columns]" + ], + "text/html": "
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" + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Use the pumpkin data from Lesso\n", + "\n", + "pumpkins = pd.read_csv('../../../Regression/data/US-pumpkins.csv')\n", + "\n", + "pumpkins.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Package Low Price High Price Price\n", + "70 0 5 3 13.636364\n", + "71 0 10 7 16.363636\n", + "72 0 10 7 16.363636\n", + "73 0 9 6 15.454545\n", + "74 0 5 3 13.636364" + ], + "text/html": "
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" + }, + "metadata": {}, + "execution_count": 23 + } + ], + "source": [ + "from sklearn.preprocessing import LabelEncoder\n", + "\n", + "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n", + "\n", + "new_columns = ['Package', 'Low Price', 'High Price']\n", + "\n", + "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n", + "\n", + "## price is the average of low and high prices\n", + "\n", + "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n", + "\n", + "new_pumpkins = pd.DataFrame({ 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n", + "\n", + "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n", + "\n", + "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n", + "\n", + "new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)\n", + "\n", + "new_pumpkins.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\nInt64Index: 415 entries, 70 to 1742\nData columns (total 4 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Package 415 non-null int64 \n 1 Low Price 415 non-null int64 \n 2 High Price 415 non-null int64 \n 3 Price 415 non-null float64\ndtypes: float64(1), int64(3)\nmemory usage: 16.2 KB\n" + ] + } + ], + "source": [ + "\n", + "new_pumpkins.dropna(inplace=True)\n", + "new_pumpkins.info()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Package Price\n", + "70 0 13.636364\n", + "71 0 16.363636\n", + "72 0 16.363636\n", + "73 0 15.454545\n", + "74 0 13.636364\n", + "... ... ...\n", + "1738 2 30.000000\n", + "1739 2 28.750000\n", + "1740 2 25.750000\n", + "1741 2 24.000000\n", + "1742 2 24.000000\n", + "\n", + "[415 rows x 2 columns]" + ], + "text/html": "
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" + }, + "metadata": {}, + "execution_count": 25 + } + ], + "source": [ + "new_columns = ['Package', 'Price']\n", + "lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')\n", + "\n", + "lin_pumpkins\n" + ] + }, + { + "source": [ + "Set X and y arrays to correspond to Package and Price" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "X = lin_pumpkins.values[:, :1]\n", + "y = lin_pumpkins.values[:, 1:2]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model Accuracy: 0.3315342327998989\n" + ] + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n", + "lin_reg = LinearRegression()\n", + "lin_reg.fit(X_train,y_train)\n", + "\n", + "pred = lin_reg.predict(X_test)\n", + "\n", + "accuracy_score = lin_reg.score(X_train,y_train)\n", + "print('Model Accuracy: ', accuracy_score)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[33.627655]]\n" + ] + } + ], + "source": [ + "import pickle\n", + "s = pickle.dumps(lin_reg)\n", + "model_filename = 'lin-reg-model.pkl'\n", + "# Open the file to save as pkl file\n", + "pickle.dump(lin_reg, open(model_filename,'wb'))\n", + "\n", + "model = pickle.load(open('lin-reg-model.pkl','rb'))\n", + "print(model.predict([[2.85]]))\n", + "\n", + "# Close the pickle instances\n", + "# clf2 = pickle.loads(s)\n", + "# clf2.predict([[2.75]])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ] +} \ No newline at end of file diff --git a/API/1-API/translations/README.es.md b/API/1-API/translations/README.es.md new file mode 100644 index 000000000..e69de29bb diff --git a/API/README.md b/API/README.md new file mode 100644 index 000000000..c153afb7c --- /dev/null +++ b/API/README.md @@ -0,0 +1,11 @@ +# Getting Started with + +In this section of the curriculum, you will be introduced to ... + +## Topics + +1. [Build an API for your model](1-API/README.md) + +## Credits + +"Build an API" was written with ♥️ by [Jen Looper](https://twitter.com/jenlooper) \ No newline at end of file diff --git a/API/translations/README.es.md b/API/translations/README.es.md new file mode 100644 index 000000000..e69de29bb diff --git a/Regression/3-Linear/solution/notebook.ipynb b/Regression/3-Linear/solution/notebook.ipynb index 8d9809c3b..438f3ef2c 100644 --- a/Regression/3-Linear/solution/notebook.ipynb +++ b/Regression/3-Linear/solution/notebook.ipynb @@ -10,13 +10,17 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3-final" + "version": "3.7.0" }, "orig_nbformat": 2, "kernelspec": { - "name": "python3", - "display_name": "Python 3", - "language": "python" + "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7", + "display_name": "Python 3.7.0 64-bit ('3.7')" + }, + "metadata": { + "interpreter": { + "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" + } } }, "nbformat": 4, @@ -38,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -71,7 +75,7 @@ "text/html": "
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\n" 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" }, "metadata": {}, - "execution_count": 6 + "execution_count": 7 } ], "source": [ @@ -276,7 +280,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -286,14 +290,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Model Accuracy: 0.3315342327998987\n" + "Model Accuracy: 0.3315342327998989\n" ] } ], @@ -315,15 +319,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
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\n" 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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -490,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -509,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -520,7 +524,7 @@ ] }, "metadata": {}, - "execution_count": 16 + "execution_count": 18 } ], "source": [