├── .gitignore
├── README.md
├── a-minimal-api
    ├── README.md
    └── api.py
├── notebooks
    ├── .ipynb_checkpoints
    │   └── test_todo-checkpoint.ipynb
    ├── sentiment_EDA.ipynb
    ├── sentiment_clf.ipynb
    ├── test_sent_clf.ipynb
    └── test_todo.ipynb
├── sentiment-clf
    ├── .gitignore
    ├── README.md
    ├── app.py
    ├── build_model.py
    ├── lib
    │   ├── __init__.py
    │   ├── data
    │   │   ├── sampleSubmission.csv
    │   │   ├── test.tsv
    │   │   └── train.tsv
    │   └── models
    │   │   ├── SentimentClassifier.pkl
    │   │   └── TFIDFVectorizer.pkl
    ├── model.py
    ├── requirements.txt
    └── util.py
└── to-do-api
    ├── README.md
    └── api.py


/.gitignore:
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1 | .ipynb_checkpoints
2 | __pycache__
3 | 


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/README.md:
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 1 | # flask-rest-setup
 2 | Notes on Flask REST API and tutorial
 3 | 
 4 | [Flask RESTful Documentation](http://flask-restful.readthedocs.io/en/latest/)
 5 | [Flask-CORS](https://flask-cors.readthedocs.io/en/latest/)
 6 | ___
 7 | 
 8 | ## Setup
 9 | 
10 | - [x] `pip install flask-restful`
11 | 


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/a-minimal-api/README.md:
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 1 | # A Minimal API
 2 | This API will return a "Hello World" JSON object
 3 | 
 4 | 
 5 | ## Usage
 6 | 1. Run the API
 7 | ```python
 8 | python api.py
 9 | ```
10 | 
11 | 2. Open a new terminal window and run the following:
12 | ```bash
13 | curl http://127.0.0.1:5000/
14 | ```
15 | 
16 | 3. This will output the following JSON object:
17 | ```
18 | {"hello": "world"}
19 | ```
20 | 
21 | ## Reference
22 | 1. [Flask RESTful Quickstart](http://flask-restful.readthedocs.io/en/latest/quickstart.html)
23 | 


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/a-minimal-api/api.py:
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 1 | from flask import Flask
 2 | from flask_restful import Resource, Api
 3 | 
 4 | app = Flask(__name__)
 5 | api = Api(app)
 6 | 
 7 | 
 8 | class HelloWorld(Resource):
 9 |     def get(self):
10 |         return {'hello': 'world'}
11 | 
12 | 
13 | api.add_resource(HelloWorld, '/')
14 | 
15 | 
16 | if __name__ == '__main__':
17 |     app.run(debug=True)
18 | 


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/notebooks/.ipynb_checkpoints/test_todo-checkpoint.ipynb:
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1 | {
2 |  "cells": [],
3 |  "metadata": {},
4 |  "nbformat": 4,
5 |  "nbformat_minor": 2
6 | }
7 | 


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/notebooks/sentiment_EDA.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 2,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "/Users/nguyen/projects/flask-rest-setup/sentiment-clf\n"
 13 |      ]
 14 |     }
 15 |    ],
 16 |    "source": [
 17 |     "cd ../sentiment-clf/"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "code",
 22 |    "execution_count": 18,
 23 |    "metadata": {},
 24 |    "outputs": [],
 25 |    "source": [
 26 |     "import pandas as pd\n",
 27 |     "import matplotlib.pyplot as plt\n",
 28 |     "import numpy as np\n",
 29 |     "%matplotlib inline\n",
 30 |     "plt.style.use('ggplot')"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 3,
 36 |    "metadata": {},
 37 |    "outputs": [],
 38 |    "source": [
 39 |     "with open('lib/data/train.tsv') as f:\n",
 40 |     "    data = pd.read_csv(f, sep='\\t')"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 11,
 46 |    "metadata": {},
 47 |    "outputs": [
 48 |     {
 49 |      "data": {
 50 |       "text/html": [
 51 |        "<div>\n",
 52 |        "<style scoped>\n",
 53 |        "    .dataframe tbody tr th:only-of-type {\n",
 54 |        "        vertical-align: middle;\n",
 55 |        "    }\n",
 56 |        "\n",
 57 |        "    .dataframe tbody tr th {\n",
 58 |        "        vertical-align: top;\n",
 59 |        "    }\n",
 60 |        "\n",
 61 |        "    .dataframe thead th {\n",
 62 |        "        text-align: right;\n",
 63 |        "    }\n",
 64 |        "</style>\n",
 65 |        "<table border=\"1\" class=\"dataframe\">\n",
 66 |        "  <thead>\n",
 67 |        "    <tr style=\"text-align: right;\">\n",
 68 |        "      <th></th>\n",
 69 |        "      <th>PhraseId</th>\n",
 70 |        "      <th>SentenceId</th>\n",
 71 |        "      <th>Phrase</th>\n",
 72 |        "      <th>Sentiment</th>\n",
 73 |        "    </tr>\n",
 74 |        "  </thead>\n",
 75 |        "  <tbody>\n",
 76 |        "    <tr>\n",
 77 |        "      <th>0</th>\n",
 78 |        "      <td>1</td>\n",
 79 |        "      <td>1</td>\n",
 80 |        "      <td>A series of escapades demonstrating the adage ...</td>\n",
 81 |        "      <td>1</td>\n",
 82 |        "    </tr>\n",
 83 |        "    <tr>\n",
 84 |        "      <th>1</th>\n",
 85 |        "      <td>2</td>\n",
 86 |        "      <td>1</td>\n",
 87 |        "      <td>A series of escapades demonstrating the adage ...</td>\n",
 88 |        "      <td>2</td>\n",
 89 |        "    </tr>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>2</th>\n",
 92 |        "      <td>3</td>\n",
 93 |        "      <td>1</td>\n",
 94 |        "      <td>A series</td>\n",
 95 |        "      <td>2</td>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>3</th>\n",
 99 |        "      <td>4</td>\n",
100 |        "      <td>1</td>\n",
101 |        "      <td>A</td>\n",
102 |        "      <td>2</td>\n",
103 |        "    </tr>\n",
104 |        "    <tr>\n",
105 |        "      <th>4</th>\n",
106 |        "      <td>5</td>\n",
107 |        "      <td>1</td>\n",
108 |        "      <td>series</td>\n",
109 |        "      <td>2</td>\n",
110 |        "    </tr>\n",
111 |        "    <tr>\n",
112 |        "      <th>5</th>\n",
113 |        "      <td>6</td>\n",
114 |        "      <td>1</td>\n",
115 |        "      <td>of escapades demonstrating the adage that what...</td>\n",
116 |        "      <td>2</td>\n",
117 |        "    </tr>\n",
118 |        "    <tr>\n",
119 |        "      <th>6</th>\n",
120 |        "      <td>7</td>\n",
121 |        "      <td>1</td>\n",
122 |        "      <td>of</td>\n",
123 |        "      <td>2</td>\n",
124 |        "    </tr>\n",
125 |        "    <tr>\n",
126 |        "      <th>7</th>\n",
127 |        "      <td>8</td>\n",
128 |        "      <td>1</td>\n",
129 |        "      <td>escapades demonstrating the adage that what is...</td>\n",
130 |        "      <td>2</td>\n",
131 |        "    </tr>\n",
132 |        "    <tr>\n",
133 |        "      <th>8</th>\n",
134 |        "      <td>9</td>\n",
135 |        "      <td>1</td>\n",
136 |        "      <td>escapades</td>\n",
137 |        "      <td>2</td>\n",
138 |        "    </tr>\n",
139 |        "    <tr>\n",
140 |        "      <th>9</th>\n",
141 |        "      <td>10</td>\n",
142 |        "      <td>1</td>\n",
143 |        "      <td>demonstrating the adage that what is good for ...</td>\n",
144 |        "      <td>2</td>\n",
145 |        "    </tr>\n",
146 |        "    <tr>\n",
147 |        "      <th>10</th>\n",
148 |        "      <td>11</td>\n",
149 |        "      <td>1</td>\n",
150 |        "      <td>demonstrating the adage</td>\n",
151 |        "      <td>2</td>\n",
152 |        "    </tr>\n",
153 |        "    <tr>\n",
154 |        "      <th>11</th>\n",
155 |        "      <td>12</td>\n",
156 |        "      <td>1</td>\n",
157 |        "      <td>demonstrating</td>\n",
158 |        "      <td>2</td>\n",
159 |        "    </tr>\n",
160 |        "    <tr>\n",
161 |        "      <th>12</th>\n",
162 |        "      <td>13</td>\n",
163 |        "      <td>1</td>\n",
164 |        "      <td>the adage</td>\n",
165 |        "      <td>2</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>13</th>\n",
169 |        "      <td>14</td>\n",
170 |        "      <td>1</td>\n",
171 |        "      <td>the</td>\n",
172 |        "      <td>2</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>14</th>\n",
176 |        "      <td>15</td>\n",
177 |        "      <td>1</td>\n",
178 |        "      <td>adage</td>\n",
179 |        "      <td>2</td>\n",
180 |        "    </tr>\n",
181 |        "    <tr>\n",
182 |        "      <th>15</th>\n",
183 |        "      <td>16</td>\n",
184 |        "      <td>1</td>\n",
185 |        "      <td>that what is good for the goose</td>\n",
186 |        "      <td>2</td>\n",
187 |        "    </tr>\n",
188 |        "    <tr>\n",
189 |        "      <th>16</th>\n",
190 |        "      <td>17</td>\n",
191 |        "      <td>1</td>\n",
192 |        "      <td>that</td>\n",
193 |        "      <td>2</td>\n",
194 |        "    </tr>\n",
195 |        "    <tr>\n",
196 |        "      <th>17</th>\n",
197 |        "      <td>18</td>\n",
198 |        "      <td>1</td>\n",
199 |        "      <td>what is good for the goose</td>\n",
200 |        "      <td>2</td>\n",
201 |        "    </tr>\n",
202 |        "    <tr>\n",
203 |        "      <th>18</th>\n",
204 |        "      <td>19</td>\n",
205 |        "      <td>1</td>\n",
206 |        "      <td>what</td>\n",
207 |        "      <td>2</td>\n",
208 |        "    </tr>\n",
209 |        "    <tr>\n",
210 |        "      <th>19</th>\n",
211 |        "      <td>20</td>\n",
212 |        "      <td>1</td>\n",
213 |        "      <td>is good for the goose</td>\n",
214 |        "      <td>2</td>\n",
215 |        "    </tr>\n",
216 |        "  </tbody>\n",
217 |        "</table>\n",
218 |        "</div>"
219 |       ],
220 |       "text/plain": [
221 |        "    PhraseId  SentenceId                                             Phrase  \\\n",
222 |        "0          1           1  A series of escapades demonstrating the adage ...   \n",
223 |        "1          2           1  A series of escapades demonstrating the adage ...   \n",
224 |        "2          3           1                                           A series   \n",
225 |        "3          4           1                                                  A   \n",
226 |        "4          5           1                                             series   \n",
227 |        "5          6           1  of escapades demonstrating the adage that what...   \n",
228 |        "6          7           1                                                 of   \n",
229 |        "7          8           1  escapades demonstrating the adage that what is...   \n",
230 |        "8          9           1                                          escapades   \n",
231 |        "9         10           1  demonstrating the adage that what is good for ...   \n",
232 |        "10        11           1                            demonstrating the adage   \n",
233 |        "11        12           1                                      demonstrating   \n",
234 |        "12        13           1                                          the adage   \n",
235 |        "13        14           1                                                the   \n",
236 |        "14        15           1                                              adage   \n",
237 |        "15        16           1                    that what is good for the goose   \n",
238 |        "16        17           1                                               that   \n",
239 |        "17        18           1                         what is good for the goose   \n",
240 |        "18        19           1                                               what   \n",
241 |        "19        20           1                              is good for the goose   \n",
242 |        "\n",
243 |        "    Sentiment  \n",
244 |        "0           1  \n",
245 |        "1           2  \n",
246 |        "2           2  \n",
247 |        "3           2  \n",
248 |        "4           2  \n",
249 |        "5           2  \n",
250 |        "6           2  \n",
251 |        "7           2  \n",
252 |        "8           2  \n",
253 |        "9           2  \n",
254 |        "10          2  \n",
255 |        "11          2  \n",
256 |        "12          2  \n",
257 |        "13          2  \n",
258 |        "14          2  \n",
259 |        "15          2  \n",
260 |        "16          2  \n",
261 |        "17          2  \n",
262 |        "18          2  \n",
263 |        "19          2  "
264 |       ]
265 |      },
266 |      "execution_count": 11,
267 |      "metadata": {},
268 |      "output_type": "execute_result"
269 |     }
270 |    ],
271 |    "source": [
272 |     "data.head(20)"
273 |    ]
274 |   },
275 |   {
276 |    "cell_type": "code",
277 |    "execution_count": 21,
278 |    "metadata": {},
279 |    "outputs": [
280 |     {
281 |      "data": {
282 |       "text/plain": [
283 |        "<matplotlib.axes._subplots.AxesSubplot at 0x1165b3e48>"
284 |       ]
285 |      },
286 |      "execution_count": 21,
287 |      "metadata": {},
288 |      "output_type": "execute_result"
289 |     },
290 |     {
291 |      "data": {
292 |       "image/png": 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C7Nmz6e/vJ5fL0d3dTTwev541S5IkSQUx6U71p59+Snd3N3feeSe//OUvAfjp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\nSUVORK5CYII=\n",
293 |       "text/plain": [
294 |        "<Figure size 864x432 with 1 Axes>"
295 |       ]
296 |      },
297 |      "metadata": {},
298 |      "output_type": "display_data"
299 |     }
300 |    ],
301 |    "source": [
302 |     "num_unique = len(data['Sentiment'].unique())\n",
303 |     "data['Sentiment'].hist(figsize=(12,6), bins=np.arange(num_unique+1)-0.5, rwidth=0.5)"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "code",
308 |    "execution_count": null,
309 |    "metadata": {},
310 |    "outputs": [],
311 |    "source": []
312 |   }
313 |  ],
314 |  "metadata": {
315 |   "kernelspec": {
316 |    "display_name": "Python 3",
317 |    "language": "python",
318 |    "name": "python3"
319 |   },
320 |   "language_info": {
321 |    "codemirror_mode": {
322 |     "name": "ipython",
323 |     "version": 3
324 |    },
325 |    "file_extension": ".py",
326 |    "mimetype": "text/x-python",
327 |    "name": "python",
328 |    "nbconvert_exporter": "python",
329 |    "pygments_lexer": "ipython3",
330 |    "version": "3.6.3"
331 |   }
332 |  },
333 |  "nbformat": 4,
334 |  "nbformat_minor": 2
335 | }
336 | 


--------------------------------------------------------------------------------
/notebooks/sentiment_clf.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "/Users/nguyen/projects/flask-rest-setup/sentiment-clf\n"
 13 |      ]
 14 |     }
 15 |    ],
 16 |    "source": [
 17 |     "cd ../sentiment-clf/"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "code",
 22 |    "execution_count": 2,
 23 |    "metadata": {},
 24 |    "outputs": [],
 25 |    "source": [
 26 |     "from model import NLPModel\n",
 27 |     "import pandas as pd\n",
 28 |     "from sklearn.model_selection import train_test_split"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "markdown",
 33 |    "metadata": {},
 34 |    "source": [
 35 |     "## Create the model object\n",
 36 |     "The NLP model object uses a Naive Bayes classifier and a TFIDF vectorizer:\n",
 37 |     "```\n",
 38 |     "self.clf = MultinomialNB()\n",
 39 |     "self.vectorizer = TfidfVectorizer()\n",
 40 |     "```"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 3,
 46 |    "metadata": {},
 47 |    "outputs": [],
 48 |    "source": [
 49 |     "model = NLPModel()"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "markdown",
 54 |    "metadata": {},
 55 |    "source": [
 56 |     "## Get the data"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "code",
 61 |    "execution_count": 4,
 62 |    "metadata": {},
 63 |    "outputs": [],
 64 |    "source": [
 65 |     "with open('lib/data/train.tsv') as f:\n",
 66 |     "    data = pd.read_csv(f, sep='\\t')"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 14,
 72 |    "metadata": {},
 73 |    "outputs": [
 74 |     {
 75 |      "data": {
 76 |       "text/html": [
 77 |        "<div>\n",
 78 |        "<style scoped>\n",
 79 |        "    .dataframe tbody tr th:only-of-type {\n",
 80 |        "        vertical-align: middle;\n",
 81 |        "    }\n",
 82 |        "\n",
 83 |        "    .dataframe tbody tr th {\n",
 84 |        "        vertical-align: top;\n",
 85 |        "    }\n",
 86 |        "\n",
 87 |        "    .dataframe thead th {\n",
 88 |        "        text-align: right;\n",
 89 |        "    }\n",
 90 |        "</style>\n",
 91 |        "<table border=\"1\" class=\"dataframe\">\n",
 92 |        "  <thead>\n",
 93 |        "    <tr style=\"text-align: right;\">\n",
 94 |        "      <th></th>\n",
 95 |        "      <th>PhraseId</th>\n",
 96 |        "      <th>SentenceId</th>\n",
 97 |        "      <th>Phrase</th>\n",
 98 |        "      <th>Sentiment</th>\n",
 99 |        "    </tr>\n",
100 |        "  </thead>\n",
101 |        "  <tbody>\n",
102 |        "    <tr>\n",
103 |        "      <th>0</th>\n",
104 |        "      <td>1</td>\n",
105 |        "      <td>1</td>\n",
106 |        "      <td>A series of escapades demonstrating the adage ...</td>\n",
107 |        "      <td>1</td>\n",
108 |        "    </tr>\n",
109 |        "    <tr>\n",
110 |        "      <th>1</th>\n",
111 |        "      <td>2</td>\n",
112 |        "      <td>1</td>\n",
113 |        "      <td>A series of escapades demonstrating the adage ...</td>\n",
114 |        "      <td>2</td>\n",
115 |        "    </tr>\n",
116 |        "    <tr>\n",
117 |        "      <th>2</th>\n",
118 |        "      <td>3</td>\n",
119 |        "      <td>1</td>\n",
120 |        "      <td>A series</td>\n",
121 |        "      <td>2</td>\n",
122 |        "    </tr>\n",
123 |        "    <tr>\n",
124 |        "      <th>3</th>\n",
125 |        "      <td>4</td>\n",
126 |        "      <td>1</td>\n",
127 |        "      <td>A</td>\n",
128 |        "      <td>2</td>\n",
129 |        "    </tr>\n",
130 |        "    <tr>\n",
131 |        "      <th>4</th>\n",
132 |        "      <td>5</td>\n",
133 |        "      <td>1</td>\n",
134 |        "      <td>series</td>\n",
135 |        "      <td>2</td>\n",
136 |        "    </tr>\n",
137 |        "    <tr>\n",
138 |        "      <th>5</th>\n",
139 |        "      <td>6</td>\n",
140 |        "      <td>1</td>\n",
141 |        "      <td>of escapades demonstrating the adage that what...</td>\n",
142 |        "      <td>2</td>\n",
143 |        "    </tr>\n",
144 |        "    <tr>\n",
145 |        "      <th>6</th>\n",
146 |        "      <td>7</td>\n",
147 |        "      <td>1</td>\n",
148 |        "      <td>of</td>\n",
149 |        "      <td>2</td>\n",
150 |        "    </tr>\n",
151 |        "    <tr>\n",
152 |        "      <th>7</th>\n",
153 |        "      <td>8</td>\n",
154 |        "      <td>1</td>\n",
155 |        "      <td>escapades demonstrating the adage that what is...</td>\n",
156 |        "      <td>2</td>\n",
157 |        "    </tr>\n",
158 |        "    <tr>\n",
159 |        "      <th>8</th>\n",
160 |        "      <td>9</td>\n",
161 |        "      <td>1</td>\n",
162 |        "      <td>escapades</td>\n",
163 |        "      <td>2</td>\n",
164 |        "    </tr>\n",
165 |        "    <tr>\n",
166 |        "      <th>9</th>\n",
167 |        "      <td>10</td>\n",
168 |        "      <td>1</td>\n",
169 |        "      <td>demonstrating the adage that what is good for ...</td>\n",
170 |        "      <td>2</td>\n",
171 |        "    </tr>\n",
172 |        "  </tbody>\n",
173 |        "</table>\n",
174 |        "</div>"
175 |       ],
176 |       "text/plain": [
177 |        "   PhraseId  SentenceId                                             Phrase  \\\n",
178 |        "0         1           1  A series of escapades demonstrating the adage ...   \n",
179 |        "1         2           1  A series of escapades demonstrating the adage ...   \n",
180 |        "2         3           1                                           A series   \n",
181 |        "3         4           1                                                  A   \n",
182 |        "4         5           1                                             series   \n",
183 |        "5         6           1  of escapades demonstrating the adage that what...   \n",
184 |        "6         7           1                                                 of   \n",
185 |        "7         8           1  escapades demonstrating the adage that what is...   \n",
186 |        "8         9           1                                          escapades   \n",
187 |        "9        10           1  demonstrating the adage that what is good for ...   \n",
188 |        "\n",
189 |        "   Sentiment  \n",
190 |        "0          1  \n",
191 |        "1          2  \n",
192 |        "2          2  \n",
193 |        "3          2  \n",
194 |        "4          2  \n",
195 |        "5          2  \n",
196 |        "6          2  \n",
197 |        "7          2  \n",
198 |        "8          2  \n",
199 |        "9          2  "
200 |       ]
201 |      },
202 |      "execution_count": 14,
203 |      "metadata": {},
204 |      "output_type": "execute_result"
205 |     }
206 |    ],
207 |    "source": [
208 |     "data.head(10)"
209 |    ]
210 |   },
211 |   {
212 |    "cell_type": "markdown",
213 |    "metadata": {},
214 |    "source": [
215 |     "## Use only the 1 star and 5 star reviews\n",
216 |     "For this example, we want to only predict positive or negative sentiment using the extreme cases."
217 |    ]
218 |   },
219 |   {
220 |    "cell_type": "code",
221 |    "execution_count": 5,
222 |    "metadata": {},
223 |    "outputs": [],
224 |    "source": [
225 |     "pos_neg = data[(data['Sentiment'] == 0) | (data['Sentiment'] == 4)]"
226 |    ]
227 |   },
228 |   {
229 |    "cell_type": "markdown",
230 |    "metadata": {},
231 |    "source": [
232 |     "## Relabel as 0 for negative and 1 for positive"
233 |    ]
234 |   },
235 |   {
236 |    "cell_type": "code",
237 |    "execution_count": 6,
238 |    "metadata": {},
239 |    "outputs": [
240 |     {
241 |      "name": "stderr",
242 |      "output_type": "stream",
243 |      "text": [
244 |       "/Users/nguyen/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
245 |       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
246 |       "Try using .loc[row_indexer,col_indexer] = value instead\n",
247 |       "\n",
248 |       "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
249 |       "  \n"
250 |      ]
251 |     }
252 |    ],
253 |    "source": [
254 |     "pos_neg['Binary'] = pos_neg.apply(\n",
255 |     "    lambda x: 0 if x['Sentiment'] == 0 else 1, axis=1)"
256 |    ]
257 |   },
258 |   {
259 |    "cell_type": "markdown",
260 |    "metadata": {},
261 |    "source": [
262 |     "## Fit a vectorizer to the vocabulary in the dataset"
263 |    ]
264 |   },
265 |   {
266 |    "cell_type": "code",
267 |    "execution_count": 7,
268 |    "metadata": {},
269 |    "outputs": [],
270 |    "source": [
271 |     "model.vectorizer_fit(pos_neg.loc[:, 'Phrase'])"
272 |    ]
273 |   },
274 |   {
275 |    "cell_type": "markdown",
276 |    "metadata": {},
277 |    "source": [
278 |     "## Transform the text in the dataset to its vectorized representation"
279 |    ]
280 |   },
281 |   {
282 |    "cell_type": "code",
283 |    "execution_count": 8,
284 |    "metadata": {},
285 |    "outputs": [],
286 |    "source": [
287 |     "X = model.vectorizer_transform(pos_neg.loc[:, 'Phrase'])"
288 |    ]
289 |   },
290 |   {
291 |    "cell_type": "markdown",
292 |    "metadata": {},
293 |    "source": [
294 |     "## Separate the target from the data"
295 |    ]
296 |   },
297 |   {
298 |    "cell_type": "code",
299 |    "execution_count": 9,
300 |    "metadata": {},
301 |    "outputs": [],
302 |    "source": [
303 |     "y = pos_neg.loc[:, 'Binary']"
304 |    ]
305 |   },
306 |   {
307 |    "cell_type": "markdown",
308 |    "metadata": {},
309 |    "source": [
310 |     "## Cross Validation!"
311 |    ]
312 |   },
313 |   {
314 |    "cell_type": "code",
315 |    "execution_count": 10,
316 |    "metadata": {},
317 |    "outputs": [],
318 |    "source": [
319 |     "X_train, X_test, y_train, y_test = train_test_split(X, y)"
320 |    ]
321 |   },
322 |   {
323 |    "cell_type": "markdown",
324 |    "metadata": {},
325 |    "source": [
326 |     "## Train the model"
327 |    ]
328 |   },
329 |   {
330 |    "cell_type": "code",
331 |    "execution_count": 11,
332 |    "metadata": {},
333 |    "outputs": [],
334 |    "source": [
335 |     "model.train(X_train, y_train)"
336 |    ]
337 |   },
338 |   {
339 |    "cell_type": "code",
340 |    "execution_count": 12,
341 |    "metadata": {},
342 |    "outputs": [],
343 |    "source": [
344 |     "from util import plot_roc"
345 |    ]
346 |   },
347 |   {
348 |    "cell_type": "code",
349 |    "execution_count": 13,
350 |    "metadata": {},
351 |    "outputs": [
352 |     {
353 |      "data": {
354 |       "image/png": 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BfPghnHsu7N4NNWuq4CUuqORFRPIzejT07QsrVsD27aHTiBRYXJZ8mTJlQkcQ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\nHwAAAABJRU5ErkJggg==\n",
355 |       "text/plain": [
356 |        "<Figure size 576x576 with 1 Axes>"
357 |       ]
358 |      },
359 |      "metadata": {},
360 |      "output_type": "display_data"
361 |     }
362 |    ],
363 |    "source": [
364 |     "model.plot_roc(X_test, y_test, size_x=8, size_y=8)"
365 |    ]
366 |   },
367 |   {
368 |    "cell_type": "code",
369 |    "execution_count": null,
370 |    "metadata": {},
371 |    "outputs": [],
372 |    "source": []
373 |   }
374 |  ],
375 |  "metadata": {
376 |   "kernelspec": {
377 |    "display_name": "Python 3",
378 |    "language": "python",
379 |    "name": "python3"
380 |   },
381 |   "language_info": {
382 |    "codemirror_mode": {
383 |     "name": "ipython",
384 |     "version": 3
385 |    },
386 |    "file_extension": ".py",
387 |    "mimetype": "text/x-python",
388 |    "name": "python",
389 |    "nbconvert_exporter": "python",
390 |    "pygments_lexer": "ipython3",
391 |    "version": "3.6.3"
392 |   }
393 |  },
394 |  "nbformat": 4,
395 |  "nbformat_minor": 2
396 | }
397 | 


--------------------------------------------------------------------------------
/notebooks/test_sent_clf.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "import requests"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "code",
 14 |    "execution_count": 4,
 15 |    "metadata": {},
 16 |    "outputs": [],
 17 |    "source": [
 18 |     "url = 'http://127.0.0.1:5000/'"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 5,
 24 |    "metadata": {},
 25 |    "outputs": [],
 26 |    "source": [
 27 |     "params ={'query': 'that movie was boring'}"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 6,
 33 |    "metadata": {},
 34 |    "outputs": [],
 35 |    "source": [
 36 |     "response = requests.get(url, params)"
 37 |    ]
 38 |   },
 39 |   {
 40 |    "cell_type": "code",
 41 |    "execution_count": 7,
 42 |    "metadata": {},
 43 |    "outputs": [
 44 |     {
 45 |      "data": {
 46 |       "text/plain": [
 47 |        "<Response [200]>"
 48 |       ]
 49 |      },
 50 |      "execution_count": 7,
 51 |      "metadata": {},
 52 |      "output_type": "execute_result"
 53 |     }
 54 |    ],
 55 |    "source": [
 56 |     "response"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "code",
 61 |    "execution_count": 8,
 62 |    "metadata": {},
 63 |    "outputs": [
 64 |     {
 65 |      "data": {
 66 |       "text/plain": [
 67 |        "{'confidence': 0.128, 'prediction': 'Negative'}"
 68 |       ]
 69 |      },
 70 |      "execution_count": 8,
 71 |      "metadata": {},
 72 |      "output_type": "execute_result"
 73 |     }
 74 |    ],
 75 |    "source": [
 76 |     "response.json()"
 77 |    ]
 78 |   },
 79 |   {
 80 |    "cell_type": "code",
 81 |    "execution_count": 9,
 82 |    "metadata": {},
 83 |    "outputs": [
 84 |     {
 85 |      "data": {
 86 |       "text/plain": [
 87 |        "{'Content-Type': 'application/json', 'Content-Length': '58', 'Server': 'Werkzeug/0.14.1 Python/3.6.3', 'Date': 'Tue, 21 Aug 2018 22:19:01 GMT'}"
 88 |       ]
 89 |      },
 90 |      "execution_count": 9,
 91 |      "metadata": {},
 92 |      "output_type": "execute_result"
 93 |     }
 94 |    ],
 95 |    "source": [
 96 |     "response.headers"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "code",
101 |    "execution_count": 10,
102 |    "metadata": {},
103 |    "outputs": [
104 |     {
105 |      "data": {
106 |       "text/plain": [
107 |        "'http://127.0.0.1:5000/?query=that+movie+was+boring'"
108 |       ]
109 |      },
110 |      "execution_count": 10,
111 |      "metadata": {},
112 |      "output_type": "execute_result"
113 |     }
114 |    ],
115 |    "source": [
116 |     "response.url"
117 |    ]
118 |   },
119 |   {
120 |    "cell_type": "code",
121 |    "execution_count": null,
122 |    "metadata": {},
123 |    "outputs": [],
124 |    "source": []
125 |   }
126 |  ],
127 |  "metadata": {
128 |   "kernelspec": {
129 |    "display_name": "Python 3",
130 |    "language": "python",
131 |    "name": "python3"
132 |   },
133 |   "language_info": {
134 |    "codemirror_mode": {
135 |     "name": "ipython",
136 |     "version": 3
137 |    },
138 |    "file_extension": ".py",
139 |    "mimetype": "text/x-python",
140 |    "name": "python",
141 |    "nbconvert_exporter": "python",
142 |    "pygments_lexer": "ipython3",
143 |    "version": "3.6.3"
144 |   }
145 |  },
146 |  "nbformat": 4,
147 |  "nbformat_minor": 2
148 | }
149 | 


--------------------------------------------------------------------------------
/notebooks/test_todo.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Implementation of Python and Requests with Flask REST API Todo Tutorial"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 1,
 13 |    "metadata": {},
 14 |    "outputs": [],
 15 |    "source": [
 16 |     "import requests"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "markdown",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "## Typical implementation for requests"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 2,
 29 |    "metadata": {},
 30 |    "outputs": [],
 31 |    "source": [
 32 |     "response = requests.get('http://localhost:5000/todos')"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 3,
 38 |    "metadata": {},
 39 |    "outputs": [
 40 |     {
 41 |      "data": {
 42 |       "text/plain": [
 43 |        "<Response [200]>"
 44 |       ]
 45 |      },
 46 |      "execution_count": 3,
 47 |      "metadata": {},
 48 |      "output_type": "execute_result"
 49 |     }
 50 |    ],
 51 |    "source": [
 52 |     "response"
 53 |    ]
 54 |   },
 55 |   {
 56 |    "cell_type": "code",
 57 |    "execution_count": 4,
 58 |    "metadata": {},
 59 |    "outputs": [
 60 |     {
 61 |      "data": {
 62 |       "text/plain": [
 63 |        "{'todo1': {'task': 'build an API'},\n",
 64 |        " 'todo2': {'task': '?????'},\n",
 65 |        " 'todo3': {'task': 'profit!'}}"
 66 |       ]
 67 |      },
 68 |      "execution_count": 4,
 69 |      "metadata": {},
 70 |      "output_type": "execute_result"
 71 |     }
 72 |    ],
 73 |    "source": [
 74 |     "response.json()"
 75 |    ]
 76 |   },
 77 |   {
 78 |    "cell_type": "markdown",
 79 |    "metadata": {},
 80 |    "source": [
 81 |     "## Update an existing task"
 82 |    ]
 83 |   },
 84 |   {
 85 |    "cell_type": "code",
 86 |    "execution_count": 5,
 87 |    "metadata": {},
 88 |    "outputs": [
 89 |     {
 90 |      "data": {
 91 |       "text/plain": [
 92 |        "{'task': 'Remember the milk'}"
 93 |       ]
 94 |      },
 95 |      "execution_count": 5,
 96 |      "metadata": {},
 97 |      "output_type": "execute_result"
 98 |     }
 99 |    ],
100 |    "source": [
101 |     "requests.put('http://localhost:5000/todos/todo1', data={'task': 'Remember the milk'}).json()"
102 |    ]
103 |   },
104 |   {
105 |    "cell_type": "markdown",
106 |    "metadata": {},
107 |    "source": [
108 |     "## Check todo list"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": 6,
114 |    "metadata": {},
115 |    "outputs": [
116 |     {
117 |      "data": {
118 |       "text/plain": [
119 |        "{'todo1': {'task': 'Remember the milk'},\n",
120 |        " 'todo2': {'task': '?????'},\n",
121 |        " 'todo3': {'task': 'profit!'}}"
122 |       ]
123 |      },
124 |      "execution_count": 6,
125 |      "metadata": {},
126 |      "output_type": "execute_result"
127 |     }
128 |    ],
129 |    "source": [
130 |     "requests.get('http://localhost:5000/todos').json()"
131 |    ]
132 |   },
133 |   {
134 |    "cell_type": "markdown",
135 |    "metadata": {},
136 |    "source": [
137 |     "## Check a specific task"
138 |    ]
139 |   },
140 |   {
141 |    "cell_type": "code",
142 |    "execution_count": 7,
143 |    "metadata": {},
144 |    "outputs": [
145 |     {
146 |      "data": {
147 |       "text/plain": [
148 |        "{'task': 'Remember the milk'}"
149 |       ]
150 |      },
151 |      "execution_count": 7,
152 |      "metadata": {},
153 |      "output_type": "execute_result"
154 |     }
155 |    ],
156 |    "source": [
157 |     "requests.get('http://localhost:5000/todos/todo1').json()"
158 |    ]
159 |   },
160 |   {
161 |    "cell_type": "markdown",
162 |    "metadata": {},
163 |    "source": [
164 |     "## Delete the task that was just created"
165 |    ]
166 |   },
167 |   {
168 |    "cell_type": "code",
169 |    "execution_count": 20,
170 |    "metadata": {},
171 |    "outputs": [
172 |     {
173 |      "data": {
174 |       "text/plain": [
175 |        "<Response [204]>"
176 |       ]
177 |      },
178 |      "execution_count": 20,
179 |      "metadata": {},
180 |      "output_type": "execute_result"
181 |     }
182 |    ],
183 |    "source": [
184 |     "requests.delete('http://localhost:5000/todos/todo4')"
185 |    ]
186 |   },
187 |   {
188 |    "cell_type": "markdown",
189 |    "metadata": {},
190 |    "source": [
191 |     "## Check todo list"
192 |    ]
193 |   },
194 |   {
195 |    "cell_type": "code",
196 |    "execution_count": 21,
197 |    "metadata": {},
198 |    "outputs": [
199 |     {
200 |      "data": {
201 |       "text/plain": [
202 |        "{'todo1': {'task': 'Remember the milk'},\n",
203 |        " 'todo2': {'task': '?????'},\n",
204 |        " 'todo3': {'task': 'profit!'}}"
205 |       ]
206 |      },
207 |      "execution_count": 21,
208 |      "metadata": {},
209 |      "output_type": "execute_result"
210 |     }
211 |    ],
212 |    "source": [
213 |     "requests.get('http://localhost:5000/todos').json()"
214 |    ]
215 |   },
216 |   {
217 |    "cell_type": "markdown",
218 |    "metadata": {},
219 |    "source": [
220 |     "## Add a new task with POST"
221 |    ]
222 |   },
223 |   {
224 |    "cell_type": "code",
225 |    "execution_count": 22,
226 |    "metadata": {},
227 |    "outputs": [
228 |     {
229 |      "data": {
230 |       "text/plain": [
231 |        "{'task': 'Buy xmas gift'}"
232 |       ]
233 |      },
234 |      "execution_count": 22,
235 |      "metadata": {},
236 |      "output_type": "execute_result"
237 |     }
238 |    ],
239 |    "source": [
240 |     "requests.post('http://localhost:5000/todos', data={'task': 'Buy xmas gift'}).json()"
241 |    ]
242 |   },
243 |   {
244 |    "cell_type": "markdown",
245 |    "metadata": {},
246 |    "source": [
247 |     "## Check todo list"
248 |    ]
249 |   },
250 |   {
251 |    "cell_type": "code",
252 |    "execution_count": 23,
253 |    "metadata": {},
254 |    "outputs": [
255 |     {
256 |      "data": {
257 |       "text/plain": [
258 |        "{'todo1': {'task': 'Remember the milk'},\n",
259 |        " 'todo2': {'task': '?????'},\n",
260 |        " 'todo3': {'task': 'profit!'},\n",
261 |        " 'todo4': {'task': 'Buy xmas gift'}}"
262 |       ]
263 |      },
264 |      "execution_count": 23,
265 |      "metadata": {},
266 |      "output_type": "execute_result"
267 |     }
268 |    ],
269 |    "source": [
270 |     "requests.get('http://localhost:5000/todos').json()"
271 |    ]
272 |   },
273 |   {
274 |    "cell_type": "code",
275 |    "execution_count": null,
276 |    "metadata": {},
277 |    "outputs": [],
278 |    "source": []
279 |   }
280 |  ],
281 |  "metadata": {
282 |   "kernelspec": {
283 |    "display_name": "Python 3",
284 |    "language": "python",
285 |    "name": "python3"
286 |   },
287 |   "language_info": {
288 |    "codemirror_mode": {
289 |     "name": "ipython",
290 |     "version": 3
291 |    },
292 |    "file_extension": ".py",
293 |    "mimetype": "text/x-python",
294 |    "name": "python",
295 |    "nbconvert_exporter": "python",
296 |    "pygments_lexer": "ipython3",
297 |    "version": "3.6.3"
298 |   }
299 |  },
300 |  "nbformat": 4,
301 |  "nbformat_minor": 2
302 | }
303 | 


--------------------------------------------------------------------------------
/sentiment-clf/.gitignore:
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1 | .chalice/deployments/
2 | .chalice/venv/
3 | 


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/sentiment-clf/README.md:
--------------------------------------------------------------------------------
 1 | # Sentiment Classifier Deployed as a REST API using Flask
 2 | 
 3 | * [Flask Restful Documentation]()
 4 | * [HTTPie Documentation](https://httpie.org/doc)
 5 | * [Data Source: Kaggle](https://www.kaggle.com/c/sentiment-analysis-on-movie-reviews/data)
 6 | ___
 7 | 
 8 | ## Procedure
 9 | 1. Start a virtual environment and install requirements
10 | 3. Build sentiment classifier
11 | 4. Write `app.py` which is the API application that will be deployed
12 | 5. Update requirements.txt as you write the code
13 | 6. Test the API
14 | 
15 | 
16 | ## File Structure
17 | * app_name
18 |   * app.py: Flask API application
19 |   * model.py: class object for classifier
20 |   * build_model.py: imports the class object from `model.py` and initiates a new model, trains the model, and pickle
21 |   * util.py: helper functions for `model.py`
22 |   * requirements.txt: list of packages that the app will import
23 |   * lib
24 |       * data: directory that contains the data files from Kaggle
25 |       * models: directory that contains the pickled model files
26 | 
27 | 
28 | ## Testing the API
29 | 1. Run the Flask API locally for testing. Go to directory with `app.py`.
30 | 
31 | ```bash
32 | python app.py
33 | ```
34 | 
35 | 
36 | 2. In a new terminal window, use HTTPie to make a GET request at the URL of the API.
37 | 
38 | ```bash
39 | http http://127.0.0.1:5000/ query=="That was pretty entertaining"
40 | ```
41 | 
42 | 
43 | 3. Example of successful output.
44 | 
45 | ```bash
46 | HTTP/1.0 200 OK
47 | Content-Length: 57
48 | Content-Type: application/json
49 | Date: Tue, 21 Aug 2018 19:04:04 GMT
50 | Server: Werkzeug/0.14.1 Python/3.6.3
51 | 
52 | {
53 |     "confidence": 0.78,
54 |     "prediction": "Positive"
55 | }
56 | ```
57 | 
58 | 4. Deploying the Flask app on an EC2 instance.
59 | 
60 | 
61 | ## Appendix
62 | 
63 | ### Virtual Environment
64 | 1. Create new virtual environment
65 | ```bash
66 | cd ~/.virtualenvs
67 | virtualenv name-of-env
68 | ```
69 | 2. Activate virtual environment
70 | ```
71 | source env/bin/activate
72 | ```
73 | 3. Go to app.py directory where `requirements.txt` is also located
74 | 4. Install required packages from `requirements.txt`
75 | ```bash
76 | pip install -r requirements.txt
77 | ```
78 | You will only have to install the `requirements.txt` when working with a new virtual environment.
79 | 


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/sentiment-clf/app.py:
--------------------------------------------------------------------------------
 1 | from flask import Flask
 2 | from flask_restful import reqparse, abort, Api, Resource
 3 | import pickle
 4 | import numpy as np
 5 | from model import NLPModel
 6 | 
 7 | app = Flask(__name__)
 8 | api = Api(app)
 9 | 
10 | model = NLPModel()
11 | 
12 | clf_path = 'lib/models/SentimentClassifier.pkl'
13 | with open(clf_path, 'rb') as f:
14 |     model.clf = pickle.load(f)
15 | 
16 | vec_path = 'lib/models/TFIDFVectorizer.pkl'
17 | with open(vec_path, 'rb') as f:
18 |     model.vectorizer = pickle.load(f)
19 | 
20 | # argument parsing
21 | parser = reqparse.RequestParser()
22 | parser.add_argument('query')
23 | 
24 | 
25 | class PredictSentiment(Resource):
26 |     def get(self):
27 |         # use parser and find the user's query
28 |         args = parser.parse_args()
29 |         user_query = args['query']
30 | 
31 |         # vectorize the user's query and make a prediction
32 |         uq_vectorized = model.vectorizer_transform(np.array([user_query]))
33 |         prediction = model.predict(uq_vectorized)
34 |         pred_proba = model.predict_proba(uq_vectorized)
35 | 
36 |         # Output either 'Negative' or 'Positive' along with the score
37 |         if prediction == 0:
38 |             pred_text = 'Negative'
39 |         else:
40 |             pred_text = 'Positive'
41 | 
42 |         # round the predict proba value and set to new variable
43 |         confidence = round(pred_proba[0], 3)
44 | 
45 |         # create JSON object
46 |         output = {'prediction': pred_text, 'confidence': confidence}
47 | 
48 |         return output
49 | 
50 | 
51 | # Setup the Api resource routing here
52 | # Route the URL to the resource
53 | api.add_resource(PredictSentiment, '/')
54 | 
55 | 
56 | if __name__ == '__main__':
57 |     app.run(debug=True)
58 | 


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/sentiment-clf/build_model.py:
--------------------------------------------------------------------------------
 1 | from model import NLPModel
 2 | import pandas as pd
 3 | # import numpy as np
 4 | # import matplotlib.pyplot as plt
 5 | from sklearn.model_selection import train_test_split
 6 | 
 7 | 
 8 | def build_model():
 9 |     model = NLPModel()
10 | 
11 |     # filename = os.path.join(
12 |     #     os.path.dirname(__file__), 'chalicelib', 'all/train.tsv')
13 |     with open('../sentiment_data/train.tsv') as f:
14 |         data = pd.read_csv(f, sep='\t')
15 | 
16 |     pos_neg = data[(data['Sentiment'] == 0) | (data['Sentiment'] == 4)]
17 | 
18 |     pos_neg['Binary'] = pos_neg.apply(
19 |         lambda x: 0 if x['Sentiment'] == 0 else 1, axis=1)
20 | 
21 |     model.vectorizer_fit(pos_neg.loc[:, 'Phrase'])
22 |     print('Vectorizer fit complete')
23 | 
24 |     X = model.vectorizer_transform(pos_neg.loc[:, 'Phrase'])
25 |     print('Vectorizer transform complete')
26 |     y = pos_neg.loc[:, 'Binary']
27 | 
28 |     X_train, X_test, y_train, y_test = train_test_split(X, y)
29 | 
30 |     model.train(X_train, y_train)
31 |     print('Model training complete')
32 | 
33 |     model.pickle_clf()
34 |     model.pickle_vectorizer()
35 | 
36 |     model.plot_roc(X_test, y_test)
37 | 
38 | 
39 | if __name__ == "__main__":
40 |     build_model()
41 | 


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/sentiment-clf/lib/__init__.py:
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https://raw.githubusercontent.com/mnguyenngo/flask-rest-setup/e64defabdf34a229aba5ea8b3fb1b7ca8a16b432/sentiment-clf/lib/__init__.py


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/sentiment-clf/lib/models/SentimentClassifier.pkl:
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/sentiment-clf/lib/models/TFIDFVectorizer.pkl:
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/sentiment-clf/model.py:
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 1 | # ML imports
 2 | from sklearn.naive_bayes import MultinomialNB
 3 | # from sklearn.naive_bayes import BernoulliNB
 4 | from sklearn.feature_extraction.text import TfidfVectorizer
 5 | 
 6 | # from sklearn.ensemble import RandomForestClassifier
 7 | import pickle
 8 | 
 9 | from util import plot_roc
10 | # spacy_tok
11 | 
12 | 
13 | class NLPModel(object):
14 | 
15 |     def __init__(self):
16 |         """Simple NLP
17 |         Attributes:
18 |             clf: sklearn classifier model
19 |             vectorizor: TFIDF vectorizer or similar
20 |         """
21 |         self.clf = MultinomialNB()
22 |         # self.vectorizer = TfidfVectorizer(tokenizer=spacy_tok)
23 |         self.vectorizer = TfidfVectorizer()
24 | 
25 |     def vectorizer_fit(self, X):
26 |         """Fits a TFIDF vectorizer to the text
27 |         """
28 |         self.vectorizer.fit(X)
29 | 
30 |     def vectorizer_transform(self, X):
31 |         """Transform the text data to a sparse TFIDF matrix
32 |         """
33 |         X_transformed = self.vectorizer.transform(X)
34 |         return X_transformed
35 | 
36 |     def train(self, X, y):
37 |         """Trains the classifier to associate the label with the sparse matrix
38 |         """
39 |         # X_train, X_test, y_train, y_test = train_test_split(X, y)
40 |         self.clf.fit(X, y)
41 | 
42 |     def predict_proba(self, X):
43 |         """Returns probability for the binary class '1' in a numpy array
44 |         """
45 |         y_proba = self.clf.predict_proba(X)
46 |         return y_proba[:, 1]
47 | 
48 |     def predict(self, X):
49 |         """Returns the predicted class in an array
50 |         """
51 |         y_pred = self.clf.predict(X)
52 |         return y_pred
53 | 
54 |     def pickle_vectorizer(self, path='chalicelib/models/TFIDFVectorizer.pkl'):
55 |         """Saves the trained vectorizer for future use.
56 |         """
57 |         with open(path, 'wb') as f:
58 |             pickle.dump(self.vectorizer, f)
59 |             print("Pickled vectorizer at {}".format(path))
60 | 
61 |     def pickle_clf(self, path='chalicelib/models/SentimentClassifier.pkl'):
62 |         """Saves the trained classifier for future use.
63 |         """
64 |         with open(path, 'wb') as f:
65 |             pickle.dump(self.clf, f)
66 |             print("Pickled classifier at {}".format(path))
67 | 
68 |     def plot_roc(self, X, y, size_x, size_y):
69 |         """Plot the ROC curve for X_test and y_test.
70 |         """
71 |         plot_roc(self.clf, X, y, size_x, size_y)
72 | 


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/sentiment-clf/requirements.txt:
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1 | chalice
2 | matplotlib
3 | sklearn
4 | numpy
5 | scipy
6 | pandas
7 | 


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/sentiment-clf/util.py:
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 1 | # import spacys
 2 | from sklearn.metrics import roc_curve, auc
 3 | import matplotlib.pyplot as plt
 4 | 
 5 | # nlp = spacy.load('en')
 6 | 
 7 | 
 8 | # def spacy_tok(text, lemmatize=False):
 9 | #     doc = nlp(text)
10 | #     if lemmatize:
11 | #         tokens = [tok.lemma_ for tok in doc]
12 | #     else:
13 | #         tokens = [tok.text for tok in doc]
14 | #     return tokens
15 | 
16 | 
17 | def plot_roc(model, x_columns, y_true, size_x=12, size_y=12):
18 |     """Returns a ROC plot
19 | 
20 |     Forked from Matt Drury.
21 |     """
22 | 
23 |     y_pred = model.predict_proba(x_columns)
24 | 
25 |     fpr, tpr, threshold = roc_curve(y_true, y_pred[:, 1])
26 |     area_under_curve = auc(fpr, tpr)
27 | 
28 |     # method I: plt
29 |     fig, ax = plt.subplots(figsize=(size_x, size_y))
30 |     model_name = str(type(model)).split('.')[-1].strip(">\'")
31 |     plt.title(f'{model_name} ROC')
32 |     ax.plot(fpr, tpr, 'k', label='AUC = %0.3f' % area_under_curve)
33 | 
34 |     ax.legend(loc='lower right')
35 |     ax.plot([0, 1], [0, 1], 'r--')
36 |     plt.xlim([0, 1])
37 |     plt.ylim([0, 1])
38 |     plt.ylabel('True Positive Rate')
39 |     plt.xlabel('False Positive Rate')
40 |     plt.show()
41 | 


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/to-do-api/README.md:
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  1 | # To Do API
  2 | 
  3 | [Flask RESTful Documentation](http://flask-restful.readthedocs.io/en/latest/quickstart.html#a-minimal-api)
  4 | 
  5 | ___
  6 | 
  7 | ## Usage
  8 | 
  9 | ### Creating Resources
 10 | ```python
 11 | # Todo
 12 | # shows a single todo item and lets you updatae or delete a todo item
 13 | class Todo(Resource):
 14 |     def get(self, todo_id):
 15 |         """Return the specified todo item given the todo_id"""
 16 |         abort_if_todo_doesnt_exist(todo_id)
 17 |         return TODOS[todo_id]
 18 | 
 19 |     def delete(self, todo_id):
 20 |         """Deletes an existing task"""
 21 |         abort_if_todo_doesnt_exist(todo_id)
 22 |         del TODOS[todo_id]
 23 |         # 204: SUCCESS; NO FURTHER CONTENT
 24 |         return '', 204
 25 | 
 26 |     def put(self, todo_id):
 27 |         """Updates existing task"""
 28 | 
 29 |         # parser
 30 |         abort_if_todo_doesnt_exist(todo_id)
 31 |         args = parser.parse_args()
 32 |         task = {'task': args['task']}
 33 |         TODOS[todo_id] = task
 34 |         # 201: CREATED
 35 |         return task, 201
 36 | 
 37 | 
 38 | # TodoList
 39 | # shows a list of all todos, and lets you POST to add new tasks
 40 | class TodoList(Resource):
 41 |     def get(self):
 42 |         """Return the current TODO dictionary"""
 43 |         return TODOS
 44 | 
 45 |     def post(self):
 46 |         """Adds task to TODO"""
 47 |         args = parser.parse_args()
 48 |         todo_id = int(max(TODOS.keys()).lstrip('todo')) + 1
 49 |         todo_id = 'todo%i' % todo_id
 50 |         TODOS[todo_id] = {'task': args['task']}
 51 |         # 201: CREATED
 52 |         return TODOS[todo_id], 201
 53 | ```
 54 | 
 55 | ### Routing to your Resources
 56 | Route the URL to the resource classes.
 57 | ```python
 58 | # Setup the Api resource routing here
 59 | api.add_resource(TodoList, '/todos')
 60 | # <todo_id> is passed as an argument to the methods of the Todo class
 61 | api.add_resource(Todo, '/todos/<todo_id>')
 62 | ```
 63 | 
 64 | ### Request Argument Parsing
 65 | [Request Parsing Docs](https://flask-restful.readthedocs.io/en/0.3.5/reqparse.html)
 66 | 
 67 | ##### Setup
 68 | Create a parser object and add the argument name 'task':
 69 | ```python
 70 | # argument parsing
 71 | parser = reqparse.RequestParser()
 72 | parser.add_argument('task')
 73 | ```
 74 | 
 75 | ##### Make Request (User)
 76 | 
 77 | 
 78 | In terminal:
 79 | ```bash
 80 | curl http://localhost:5000/todos/todo3 -d "task=something different" -X PUT -v
 81 | ```
 82 | 
 83 | With Python using Requests:
 84 | ```python
 85 | requests.put('http://localhost:5000/todos/todo1', data={'task': 'Remember the milk'}).json()
 86 | ```
 87 | 
 88 | ##### Parsing Request (API)
 89 | args is a dictionary of the data that is passed through requests or curl.
 90 | ```python
 91 | args = parser.parse_args()
 92 | task = {'task': args['task']}
 93 | # add task to the TODOS dictionary
 94 | TODOS[todo_id] = task
 95 | ```
 96 | 
 97 | 
 98 | ### Aborting a Request
 99 | #### Function Definition
100 | ```python
101 | def abort_if_todo_doesnt_exist(todo_id):
102 |     """Abort request if todo_id does not exist in TODOS"""
103 |     if todo_id not in TODOS:
104 |         abort(404, message="Todo {} doesn't exist".format(todo_id))
105 | ```
106 | 
107 | #### Implement in a request method
108 | ```python
109 | def delete(self, todo_id):
110 |     """Deletes an existing task"""
111 |     abort_if_todo_doesnt_exist(todo_id)
112 |     del TODOS[todo_id]
113 |     # 204: SUCCESS; NO FURTHER CONTENT
114 |     return '', 204
115 | ```
116 | 


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/to-do-api/api.py:
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  1 | from flask import Flask
  2 | from flask_restful import reqparse, abort, Api, Resource
  3 | 
  4 | app = Flask(__name__)
  5 | api = Api(app)
  6 | 
  7 | TODOS = {
  8 |     'todo1': {'task': 'build an API'},
  9 |     'todo2': {'task': '?????'},
 10 |     'todo3': {'task': 'profit!'},
 11 | }
 12 | 
 13 | 
 14 | def abort_if_todo_doesnt_exist(todo_id):
 15 |     """Abort request if todo_id does not exist in TODOS"""
 16 |     if todo_id not in TODOS:
 17 |         abort(404, message="Todo {} doesn't exist".format(todo_id))
 18 | 
 19 | 
 20 | # argument parsing
 21 | parser = reqparse.RequestParser()
 22 | parser.add_argument('task')
 23 | 
 24 | 
 25 | # Todo
 26 | # shows a single todo item and lets you updatae or delete a todo item
 27 | class Todo(Resource):
 28 |     def get(self, todo_id):
 29 |         """Return the specified todo item given the todo_id
 30 | 
 31 |         Example:
 32 |             # In the terminal
 33 |             $ curl http://localhost:5000/todos/todo1
 34 | 
 35 |             OR
 36 | 
 37 |             # Python
 38 |             requests.get('http://localhost:5000/todos/todo1').json()
 39 |         """
 40 |         abort_if_todo_doesnt_exist(todo_id)
 41 |         return TODOS[todo_id]
 42 | 
 43 |     def delete(self, todo_id):
 44 |         """Deletes an existing task
 45 | 
 46 |         Example:
 47 |             # In the terminal
 48 |             $ curl http://localhost:5000/todos/todo1 -X DELETE -v
 49 | 
 50 |             OR
 51 | 
 52 |             # Python
 53 |             requests.delete('http://localhost:5000/todos/todo4')
 54 |         """
 55 |         abort_if_todo_doesnt_exist(todo_id)
 56 |         del TODOS[todo_id]
 57 |         # 204: SUCCESS; NO FURTHER CONTENT
 58 |         return '', 204
 59 | 
 60 |     def put(self, todo_id):
 61 |         """Updates existing task
 62 | 
 63 |         Example:
 64 |             # In the terminal
 65 |             $ curl http://localhost:5000/todos/todo1 -d "task=Remember the milk" -X PUT -v
 66 | 
 67 |             OR
 68 | 
 69 |             # Python
 70 |             requests.put('http://localhost:5000/todos/todo1',
 71 |                          data={'task': 'Remember the milk'}).json()
 72 |         """
 73 | 
 74 |         # parser
 75 |         abort_if_todo_doesnt_exist(todo_id)
 76 |         args = parser.parse_args()
 77 |         task = {'task': args['task']}
 78 |         TODOS[todo_id] = task
 79 |         # 201: CREATED
 80 |         return task, 201
 81 | 
 82 | 
 83 | # TodoList
 84 | # shows a list of all todos, and lets you POST to add new tasks
 85 | class TodoList(Resource):
 86 |     def get(self):
 87 |         """Return the current TODO dictionary
 88 | 
 89 |         Example:
 90 |             # In the terminal
 91 |             $ curl http://localhost:5000/todos
 92 | 
 93 |             OR
 94 | 
 95 |             # Python
 96 |             requests.get('http://localhost:5000/todos').json()
 97 |         """
 98 |         return TODOS
 99 | 
100 |     def post(self):
101 |         """Adds task to TODO
102 | 
103 |         Example:
104 |             # In the terminal
105 |             $ curl http://localhost:5000/todos -d "task=Remember the milk" -X POST -v
106 | 
107 |             OR
108 | 
109 |             # Python
110 |             requests.post('http://localhost:5000/todos',
111 |                          data={'task': 'Remember the milk'}).json()
112 |         """
113 |         args = parser.parse_args()
114 |         todo_id = int(max(TODOS.keys()).lstrip('todo')) + 1
115 |         todo_id = 'todo%i' % todo_id
116 |         TODOS[todo_id] = {'task': args['task']}
117 |         # 201: CREATED
118 |         return TODOS[todo_id], 201
119 | 
120 | 
121 | # Setup the Api resource routing here
122 | # Route the URL to the resource
123 | api.add_resource(TodoList, '/todos')
124 | api.add_resource(Todo, '/todos/<todo_id>')
125 | 
126 | 
127 | if __name__ == '__main__':
128 |     app.run(debug=True)
129 | 


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