Spaces:
Runtime error
Runtime error
training code
Browse files- src/app.py → app.py +0 -0
- src/model_training_v2.ipynb +1226 -0
src/app.py → app.py
RENAMED
File without changes
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src/model_training_v2.ipynb
ADDED
@@ -0,0 +1,1226 @@
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|
1 |
+
{
|
2 |
+
"nbformat": 4,
|
3 |
+
"nbformat_minor": 0,
|
4 |
+
"metadata": {
|
5 |
+
"colab": {
|
6 |
+
"provenance": []
|
7 |
+
},
|
8 |
+
"kernelspec": {
|
9 |
+
"name": "python3",
|
10 |
+
"display_name": "Python 3"
|
11 |
+
},
|
12 |
+
"language_info": {
|
13 |
+
"name": "python"
|
14 |
+
}
|
15 |
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},
|
16 |
+
"cells": [
|
17 |
+
{
|
18 |
+
"cell_type": "markdown",
|
19 |
+
"source": [
|
20 |
+
"# **Music recommender**"
|
21 |
+
],
|
22 |
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"metadata": {
|
23 |
+
"id": "DDADPl-phDUC"
|
24 |
+
}
|
25 |
+
},
|
26 |
+
{
|
27 |
+
"cell_type": "markdown",
|
28 |
+
"source": [
|
29 |
+
"# **Load Data**"
|
30 |
+
],
|
31 |
+
"metadata": {
|
32 |
+
"id": "E7Cu5Fmqct7J"
|
33 |
+
}
|
34 |
+
},
|
35 |
+
{
|
36 |
+
"cell_type": "code",
|
37 |
+
"execution_count": null,
|
38 |
+
"metadata": {
|
39 |
+
"colab": {
|
40 |
+
"base_uri": "https://localhost:8080/",
|
41 |
+
"height": 540
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42 |
+
},
|
43 |
+
"id": "bI8bNavbajsv",
|
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"outputId": "7cba8b5d-4a63-433f-be3c-87ce794833ba"
|
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},
|
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"outputs": [
|
47 |
+
{
|
48 |
+
"output_type": "display_data",
|
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+
"data": {
|
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+
"text/plain": [
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"<IPython.core.display.HTML object>"
|
52 |
+
],
|
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+
"text/html": [
|
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"\n",
|
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" <input type=\"file\" id=\"files-793c32c8-99a6-4873-9585-738e1d4b2ab1\" name=\"files[]\" multiple disabled\n",
|
56 |
+
" style=\"border:none\" />\n",
|
57 |
+
" <output id=\"result-793c32c8-99a6-4873-9585-738e1d4b2ab1\">\n",
|
58 |
+
" Upload widget is only available when the cell has been executed in the\n",
|
59 |
+
" current browser session. Please rerun this cell to enable.\n",
|
60 |
+
" </output>\n",
|
61 |
+
" <script>// Copyright 2017 Google LLC\n",
|
62 |
+
"//\n",
|
63 |
+
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
|
64 |
+
"// you may not use this file except in compliance with the License.\n",
|
65 |
+
"// You may obtain a copy of the License at\n",
|
66 |
+
"//\n",
|
67 |
+
"// http://www.apache.org/licenses/LICENSE-2.0\n",
|
68 |
+
"//\n",
|
69 |
+
"// Unless required by applicable law or agreed to in writing, software\n",
|
70 |
+
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
|
71 |
+
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
|
72 |
+
"// See the License for the specific language governing permissions and\n",
|
73 |
+
"// limitations under the License.\n",
|
74 |
+
"\n",
|
75 |
+
"/**\n",
|
76 |
+
" * @fileoverview Helpers for google.colab Python module.\n",
|
77 |
+
" */\n",
|
78 |
+
"(function(scope) {\n",
|
79 |
+
"function span(text, styleAttributes = {}) {\n",
|
80 |
+
" const element = document.createElement('span');\n",
|
81 |
+
" element.textContent = text;\n",
|
82 |
+
" for (const key of Object.keys(styleAttributes)) {\n",
|
83 |
+
" element.style[key] = styleAttributes[key];\n",
|
84 |
+
" }\n",
|
85 |
+
" return element;\n",
|
86 |
+
"}\n",
|
87 |
+
"\n",
|
88 |
+
"// Max number of bytes which will be uploaded at a time.\n",
|
89 |
+
"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
|
90 |
+
"\n",
|
91 |
+
"function _uploadFiles(inputId, outputId) {\n",
|
92 |
+
" const steps = uploadFilesStep(inputId, outputId);\n",
|
93 |
+
" const outputElement = document.getElementById(outputId);\n",
|
94 |
+
" // Cache steps on the outputElement to make it available for the next call\n",
|
95 |
+
" // to uploadFilesContinue from Python.\n",
|
96 |
+
" outputElement.steps = steps;\n",
|
97 |
+
"\n",
|
98 |
+
" return _uploadFilesContinue(outputId);\n",
|
99 |
+
"}\n",
|
100 |
+
"\n",
|
101 |
+
"// This is roughly an async generator (not supported in the browser yet),\n",
|
102 |
+
"// where there are multiple asynchronous steps and the Python side is going\n",
|
103 |
+
"// to poll for completion of each step.\n",
|
104 |
+
"// This uses a Promise to block the python side on completion of each step,\n",
|
105 |
+
"// then passes the result of the previous step as the input to the next step.\n",
|
106 |
+
"function _uploadFilesContinue(outputId) {\n",
|
107 |
+
" const outputElement = document.getElementById(outputId);\n",
|
108 |
+
" const steps = outputElement.steps;\n",
|
109 |
+
"\n",
|
110 |
+
" const next = steps.next(outputElement.lastPromiseValue);\n",
|
111 |
+
" return Promise.resolve(next.value.promise).then((value) => {\n",
|
112 |
+
" // Cache the last promise value to make it available to the next\n",
|
113 |
+
" // step of the generator.\n",
|
114 |
+
" outputElement.lastPromiseValue = value;\n",
|
115 |
+
" return next.value.response;\n",
|
116 |
+
" });\n",
|
117 |
+
"}\n",
|
118 |
+
"\n",
|
119 |
+
"/**\n",
|
120 |
+
" * Generator function which is called between each async step of the upload\n",
|
121 |
+
" * process.\n",
|
122 |
+
" * @param {string} inputId Element ID of the input file picker element.\n",
|
123 |
+
" * @param {string} outputId Element ID of the output display.\n",
|
124 |
+
" * @return {!Iterable<!Object>} Iterable of next steps.\n",
|
125 |
+
" */\n",
|
126 |
+
"function* uploadFilesStep(inputId, outputId) {\n",
|
127 |
+
" const inputElement = document.getElementById(inputId);\n",
|
128 |
+
" inputElement.disabled = false;\n",
|
129 |
+
"\n",
|
130 |
+
" const outputElement = document.getElementById(outputId);\n",
|
131 |
+
" outputElement.innerHTML = '';\n",
|
132 |
+
"\n",
|
133 |
+
" const pickedPromise = new Promise((resolve) => {\n",
|
134 |
+
" inputElement.addEventListener('change', (e) => {\n",
|
135 |
+
" resolve(e.target.files);\n",
|
136 |
+
" });\n",
|
137 |
+
" });\n",
|
138 |
+
"\n",
|
139 |
+
" const cancel = document.createElement('button');\n",
|
140 |
+
" inputElement.parentElement.appendChild(cancel);\n",
|
141 |
+
" cancel.textContent = 'Cancel upload';\n",
|
142 |
+
" const cancelPromise = new Promise((resolve) => {\n",
|
143 |
+
" cancel.onclick = () => {\n",
|
144 |
+
" resolve(null);\n",
|
145 |
+
" };\n",
|
146 |
+
" });\n",
|
147 |
+
"\n",
|
148 |
+
" // Wait for the user to pick the files.\n",
|
149 |
+
" const files = yield {\n",
|
150 |
+
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
|
151 |
+
" response: {\n",
|
152 |
+
" action: 'starting',\n",
|
153 |
+
" }\n",
|
154 |
+
" };\n",
|
155 |
+
"\n",
|
156 |
+
" cancel.remove();\n",
|
157 |
+
"\n",
|
158 |
+
" // Disable the input element since further picks are not allowed.\n",
|
159 |
+
" inputElement.disabled = true;\n",
|
160 |
+
"\n",
|
161 |
+
" if (!files) {\n",
|
162 |
+
" return {\n",
|
163 |
+
" response: {\n",
|
164 |
+
" action: 'complete',\n",
|
165 |
+
" }\n",
|
166 |
+
" };\n",
|
167 |
+
" }\n",
|
168 |
+
"\n",
|
169 |
+
" for (const file of files) {\n",
|
170 |
+
" const li = document.createElement('li');\n",
|
171 |
+
" li.append(span(file.name, {fontWeight: 'bold'}));\n",
|
172 |
+
" li.append(span(\n",
|
173 |
+
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
|
174 |
+
" `last modified: ${\n",
|
175 |
+
" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
|
176 |
+
" 'n/a'} - `));\n",
|
177 |
+
" const percent = span('0% done');\n",
|
178 |
+
" li.appendChild(percent);\n",
|
179 |
+
"\n",
|
180 |
+
" outputElement.appendChild(li);\n",
|
181 |
+
"\n",
|
182 |
+
" const fileDataPromise = new Promise((resolve) => {\n",
|
183 |
+
" const reader = new FileReader();\n",
|
184 |
+
" reader.onload = (e) => {\n",
|
185 |
+
" resolve(e.target.result);\n",
|
186 |
+
" };\n",
|
187 |
+
" reader.readAsArrayBuffer(file);\n",
|
188 |
+
" });\n",
|
189 |
+
" // Wait for the data to be ready.\n",
|
190 |
+
" let fileData = yield {\n",
|
191 |
+
" promise: fileDataPromise,\n",
|
192 |
+
" response: {\n",
|
193 |
+
" action: 'continue',\n",
|
194 |
+
" }\n",
|
195 |
+
" };\n",
|
196 |
+
"\n",
|
197 |
+
" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
|
198 |
+
" let position = 0;\n",
|
199 |
+
" do {\n",
|
200 |
+
" const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
|
201 |
+
" const chunk = new Uint8Array(fileData, position, length);\n",
|
202 |
+
" position += length;\n",
|
203 |
+
"\n",
|
204 |
+
" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
|
205 |
+
" yield {\n",
|
206 |
+
" response: {\n",
|
207 |
+
" action: 'append',\n",
|
208 |
+
" file: file.name,\n",
|
209 |
+
" data: base64,\n",
|
210 |
+
" },\n",
|
211 |
+
" };\n",
|
212 |
+
"\n",
|
213 |
+
" let percentDone = fileData.byteLength === 0 ?\n",
|
214 |
+
" 100 :\n",
|
215 |
+
" Math.round((position / fileData.byteLength) * 100);\n",
|
216 |
+
" percent.textContent = `${percentDone}% done`;\n",
|
217 |
+
"\n",
|
218 |
+
" } while (position < fileData.byteLength);\n",
|
219 |
+
" }\n",
|
220 |
+
"\n",
|
221 |
+
" // All done.\n",
|
222 |
+
" yield {\n",
|
223 |
+
" response: {\n",
|
224 |
+
" action: 'complete',\n",
|
225 |
+
" }\n",
|
226 |
+
" };\n",
|
227 |
+
"}\n",
|
228 |
+
"\n",
|
229 |
+
"scope.google = scope.google || {};\n",
|
230 |
+
"scope.google.colab = scope.google.colab || {};\n",
|
231 |
+
"scope.google.colab._files = {\n",
|
232 |
+
" _uploadFiles,\n",
|
233 |
+
" _uploadFilesContinue,\n",
|
234 |
+
"};\n",
|
235 |
+
"})(self);\n",
|
236 |
+
"</script> "
|
237 |
+
]
|
238 |
+
},
|
239 |
+
"metadata": {}
|
240 |
+
},
|
241 |
+
{
|
242 |
+
"output_type": "stream",
|
243 |
+
"name": "stdout",
|
244 |
+
"text": [
|
245 |
+
"Saving music_data.csv to music_data.csv\n",
|
246 |
+
" title \\\n",
|
247 |
+
"0 100 Club 1996 ''We Love You Beatles'' - Live \n",
|
248 |
+
"1 Yo Quiero Contigo \n",
|
249 |
+
"4 Emerald \n",
|
250 |
+
"6 Karma \n",
|
251 |
+
"7 Money Blues \n",
|
252 |
+
"\n",
|
253 |
+
" release artist_name duration \\\n",
|
254 |
+
"0 Sex Pistols - The Interviews Sex Pistols 88.73751 \n",
|
255 |
+
"1 Sentenciados - Platinum Edition Baby Rasta & Gringo 167.36608 \n",
|
256 |
+
"4 Emerald Bedrock 501.86404 \n",
|
257 |
+
"6 The Diary Of Alicia Keys Alicia Keys 255.99955 \n",
|
258 |
+
"7 Slidetime Joanna Connor 243.66975 \n",
|
259 |
+
"\n",
|
260 |
+
" artist_familiarity artist_hotttnesss year listeners playcount \\\n",
|
261 |
+
"0 0.731184 0.549204 0 172 210 \n",
|
262 |
+
"1 0.610186 0.355320 0 9753 16911 \n",
|
263 |
+
"4 0.654039 0.390625 2004 973 2247 \n",
|
264 |
+
"6 0.933916 0.778674 2003 250304 1028356 \n",
|
265 |
+
"7 0.479218 0.332857 0 429 1008 \n",
|
266 |
+
"\n",
|
267 |
+
" tags \n",
|
268 |
+
"0 The Beatles, title is a full sentence \n",
|
269 |
+
"1 Reggaeton, alexis y fido, Eliana, mis videos, ... \n",
|
270 |
+
"4 dance \n",
|
271 |
+
"6 rnb, soul, Alicia Keys, female vocalists, Karma \n",
|
272 |
+
"7 guitar girl, blues \n"
|
273 |
+
]
|
274 |
+
}
|
275 |
+
],
|
276 |
+
"source": [
|
277 |
+
"import pandas as pd\n",
|
278 |
+
"from google.colab import files\n",
|
279 |
+
"\n",
|
280 |
+
"# Upload the file\n",
|
281 |
+
"uploaded = files.upload()\n",
|
282 |
+
"\n",
|
283 |
+
"# Assuming the file is named \"music_data.csv\"\n",
|
284 |
+
"data_path = \"music_data.csv\"\n",
|
285 |
+
"\n",
|
286 |
+
"# Load the data\n",
|
287 |
+
"df = pd.read_csv(data_path)\n",
|
288 |
+
"df.dropna(inplace=True)\n",
|
289 |
+
"\n",
|
290 |
+
"# Display the first few rows of the dataset\n",
|
291 |
+
"print(df.head())\n"
|
292 |
+
]
|
293 |
+
},
|
294 |
+
{
|
295 |
+
"cell_type": "code",
|
296 |
+
"source": [
|
297 |
+
"df.head()"
|
298 |
+
],
|
299 |
+
"metadata": {
|
300 |
+
"colab": {
|
301 |
+
"base_uri": "https://localhost:8080/",
|
302 |
+
"height": 206
|
303 |
+
},
|
304 |
+
"id": "9E3in0U3dK5I",
|
305 |
+
"outputId": "c1d5362a-6a33-4543-ff4d-4e11cf8220ec"
|
306 |
+
},
|
307 |
+
"execution_count": null,
|
308 |
+
"outputs": [
|
309 |
+
{
|
310 |
+
"output_type": "execute_result",
|
311 |
+
"data": {
|
312 |
+
"text/plain": [
|
313 |
+
" title \\\n",
|
314 |
+
"0 100 Club 1996 ''We Love You Beatles'' - Live \n",
|
315 |
+
"1 Yo Quiero Contigo \n",
|
316 |
+
"4 Emerald \n",
|
317 |
+
"6 Karma \n",
|
318 |
+
"7 Money Blues \n",
|
319 |
+
"\n",
|
320 |
+
" release artist_name duration \\\n",
|
321 |
+
"0 Sex Pistols - The Interviews Sex Pistols 88.73751 \n",
|
322 |
+
"1 Sentenciados - Platinum Edition Baby Rasta & Gringo 167.36608 \n",
|
323 |
+
"4 Emerald Bedrock 501.86404 \n",
|
324 |
+
"6 The Diary Of Alicia Keys Alicia Keys 255.99955 \n",
|
325 |
+
"7 Slidetime Joanna Connor 243.66975 \n",
|
326 |
+
"\n",
|
327 |
+
" artist_familiarity artist_hotttnesss year listeners playcount \\\n",
|
328 |
+
"0 0.731184 0.549204 0 172 210 \n",
|
329 |
+
"1 0.610186 0.355320 0 9753 16911 \n",
|
330 |
+
"4 0.654039 0.390625 2004 973 2247 \n",
|
331 |
+
"6 0.933916 0.778674 2003 250304 1028356 \n",
|
332 |
+
"7 0.479218 0.332857 0 429 1008 \n",
|
333 |
+
"\n",
|
334 |
+
" tags \n",
|
335 |
+
"0 The Beatles, title is a full sentence \n",
|
336 |
+
"1 Reggaeton, alexis y fido, Eliana, mis videos, ... \n",
|
337 |
+
"4 dance \n",
|
338 |
+
"6 rnb, soul, Alicia Keys, female vocalists, Karma \n",
|
339 |
+
"7 guitar girl, blues "
|
340 |
+
],
|
341 |
+
"text/html": [
|
342 |
+
"\n",
|
343 |
+
" <div id=\"df-b9e5c35d-1534-4ad7-8661-887b39a472e9\" class=\"colab-df-container\">\n",
|
344 |
+
" <div>\n",
|
345 |
+
"<style scoped>\n",
|
346 |
+
" .dataframe tbody tr th:only-of-type {\n",
|
347 |
+
" vertical-align: middle;\n",
|
348 |
+
" }\n",
|
349 |
+
"\n",
|
350 |
+
" .dataframe tbody tr th {\n",
|
351 |
+
" vertical-align: top;\n",
|
352 |
+
" }\n",
|
353 |
+
"\n",
|
354 |
+
" .dataframe thead th {\n",
|
355 |
+
" text-align: right;\n",
|
356 |
+
" }\n",
|
357 |
+
"</style>\n",
|
358 |
+
"<table border=\"1\" class=\"dataframe\">\n",
|
359 |
+
" <thead>\n",
|
360 |
+
" <tr style=\"text-align: right;\">\n",
|
361 |
+
" <th></th>\n",
|
362 |
+
" <th>title</th>\n",
|
363 |
+
" <th>release</th>\n",
|
364 |
+
" <th>artist_name</th>\n",
|
365 |
+
" <th>duration</th>\n",
|
366 |
+
" <th>artist_familiarity</th>\n",
|
367 |
+
" <th>artist_hotttnesss</th>\n",
|
368 |
+
" <th>year</th>\n",
|
369 |
+
" <th>listeners</th>\n",
|
370 |
+
" <th>playcount</th>\n",
|
371 |
+
" <th>tags</th>\n",
|
372 |
+
" </tr>\n",
|
373 |
+
" </thead>\n",
|
374 |
+
" <tbody>\n",
|
375 |
+
" <tr>\n",
|
376 |
+
" <th>0</th>\n",
|
377 |
+
" <td>100 Club 1996 ''We Love You Beatles'' - Live</td>\n",
|
378 |
+
" <td>Sex Pistols - The Interviews</td>\n",
|
379 |
+
" <td>Sex Pistols</td>\n",
|
380 |
+
" <td>88.73751</td>\n",
|
381 |
+
" <td>0.731184</td>\n",
|
382 |
+
" <td>0.549204</td>\n",
|
383 |
+
" <td>0</td>\n",
|
384 |
+
" <td>172</td>\n",
|
385 |
+
" <td>210</td>\n",
|
386 |
+
" <td>The Beatles, title is a full sentence</td>\n",
|
387 |
+
" </tr>\n",
|
388 |
+
" <tr>\n",
|
389 |
+
" <th>1</th>\n",
|
390 |
+
" <td>Yo Quiero Contigo</td>\n",
|
391 |
+
" <td>Sentenciados - Platinum Edition</td>\n",
|
392 |
+
" <td>Baby Rasta & Gringo</td>\n",
|
393 |
+
" <td>167.36608</td>\n",
|
394 |
+
" <td>0.610186</td>\n",
|
395 |
+
" <td>0.355320</td>\n",
|
396 |
+
" <td>0</td>\n",
|
397 |
+
" <td>9753</td>\n",
|
398 |
+
" <td>16911</td>\n",
|
399 |
+
" <td>Reggaeton, alexis y fido, Eliana, mis videos, ...</td>\n",
|
400 |
+
" </tr>\n",
|
401 |
+
" <tr>\n",
|
402 |
+
" <th>4</th>\n",
|
403 |
+
" <td>Emerald</td>\n",
|
404 |
+
" <td>Emerald</td>\n",
|
405 |
+
" <td>Bedrock</td>\n",
|
406 |
+
" <td>501.86404</td>\n",
|
407 |
+
" <td>0.654039</td>\n",
|
408 |
+
" <td>0.390625</td>\n",
|
409 |
+
" <td>2004</td>\n",
|
410 |
+
" <td>973</td>\n",
|
411 |
+
" <td>2247</td>\n",
|
412 |
+
" <td>dance</td>\n",
|
413 |
+
" </tr>\n",
|
414 |
+
" <tr>\n",
|
415 |
+
" <th>6</th>\n",
|
416 |
+
" <td>Karma</td>\n",
|
417 |
+
" <td>The Diary Of Alicia Keys</td>\n",
|
418 |
+
" <td>Alicia Keys</td>\n",
|
419 |
+
" <td>255.99955</td>\n",
|
420 |
+
" <td>0.933916</td>\n",
|
421 |
+
" <td>0.778674</td>\n",
|
422 |
+
" <td>2003</td>\n",
|
423 |
+
" <td>250304</td>\n",
|
424 |
+
" <td>1028356</td>\n",
|
425 |
+
" <td>rnb, soul, Alicia Keys, female vocalists, Karma</td>\n",
|
426 |
+
" </tr>\n",
|
427 |
+
" <tr>\n",
|
428 |
+
" <th>7</th>\n",
|
429 |
+
" <td>Money Blues</td>\n",
|
430 |
+
" <td>Slidetime</td>\n",
|
431 |
+
" <td>Joanna Connor</td>\n",
|
432 |
+
" <td>243.66975</td>\n",
|
433 |
+
" <td>0.479218</td>\n",
|
434 |
+
" <td>0.332857</td>\n",
|
435 |
+
" <td>0</td>\n",
|
436 |
+
" <td>429</td>\n",
|
437 |
+
" <td>1008</td>\n",
|
438 |
+
" <td>guitar girl, blues</td>\n",
|
439 |
+
" </tr>\n",
|
440 |
+
" </tbody>\n",
|
441 |
+
"</table>\n",
|
442 |
+
"</div>\n",
|
443 |
+
" <div class=\"colab-df-buttons\">\n",
|
444 |
+
"\n",
|
445 |
+
" <div class=\"colab-df-container\">\n",
|
446 |
+
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b9e5c35d-1534-4ad7-8661-887b39a472e9')\"\n",
|
447 |
+
" title=\"Convert this dataframe to an interactive table.\"\n",
|
448 |
+
" style=\"display:none;\">\n",
|
449 |
+
"\n",
|
450 |
+
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
|
451 |
+
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
|
452 |
+
" </svg>\n",
|
453 |
+
" </button>\n",
|
454 |
+
"\n",
|
455 |
+
" <style>\n",
|
456 |
+
" .colab-df-container {\n",
|
457 |
+
" display:flex;\n",
|
458 |
+
" gap: 12px;\n",
|
459 |
+
" }\n",
|
460 |
+
"\n",
|
461 |
+
" .colab-df-convert {\n",
|
462 |
+
" background-color: #E8F0FE;\n",
|
463 |
+
" border: none;\n",
|
464 |
+
" border-radius: 50%;\n",
|
465 |
+
" cursor: pointer;\n",
|
466 |
+
" display: none;\n",
|
467 |
+
" fill: #1967D2;\n",
|
468 |
+
" height: 32px;\n",
|
469 |
+
" padding: 0 0 0 0;\n",
|
470 |
+
" width: 32px;\n",
|
471 |
+
" }\n",
|
472 |
+
"\n",
|
473 |
+
" .colab-df-convert:hover {\n",
|
474 |
+
" background-color: #E2EBFA;\n",
|
475 |
+
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
476 |
+
" fill: #174EA6;\n",
|
477 |
+
" }\n",
|
478 |
+
"\n",
|
479 |
+
" .colab-df-buttons div {\n",
|
480 |
+
" margin-bottom: 4px;\n",
|
481 |
+
" }\n",
|
482 |
+
"\n",
|
483 |
+
" [theme=dark] .colab-df-convert {\n",
|
484 |
+
" background-color: #3B4455;\n",
|
485 |
+
" fill: #D2E3FC;\n",
|
486 |
+
" }\n",
|
487 |
+
"\n",
|
488 |
+
" [theme=dark] .colab-df-convert:hover {\n",
|
489 |
+
" background-color: #434B5C;\n",
|
490 |
+
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
491 |
+
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
492 |
+
" fill: #FFFFFF;\n",
|
493 |
+
" }\n",
|
494 |
+
" </style>\n",
|
495 |
+
"\n",
|
496 |
+
" <script>\n",
|
497 |
+
" const buttonEl =\n",
|
498 |
+
" document.querySelector('#df-b9e5c35d-1534-4ad7-8661-887b39a472e9 button.colab-df-convert');\n",
|
499 |
+
" buttonEl.style.display =\n",
|
500 |
+
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
501 |
+
"\n",
|
502 |
+
" async function convertToInteractive(key) {\n",
|
503 |
+
" const element = document.querySelector('#df-b9e5c35d-1534-4ad7-8661-887b39a472e9');\n",
|
504 |
+
" const dataTable =\n",
|
505 |
+
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
506 |
+
" [key], {});\n",
|
507 |
+
" if (!dataTable) return;\n",
|
508 |
+
"\n",
|
509 |
+
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
510 |
+
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
511 |
+
" + ' to learn more about interactive tables.';\n",
|
512 |
+
" element.innerHTML = '';\n",
|
513 |
+
" dataTable['output_type'] = 'display_data';\n",
|
514 |
+
" await google.colab.output.renderOutput(dataTable, element);\n",
|
515 |
+
" const docLink = document.createElement('div');\n",
|
516 |
+
" docLink.innerHTML = docLinkHtml;\n",
|
517 |
+
" element.appendChild(docLink);\n",
|
518 |
+
" }\n",
|
519 |
+
" </script>\n",
|
520 |
+
" </div>\n",
|
521 |
+
"\n",
|
522 |
+
"\n",
|
523 |
+
"<div id=\"df-3ffda883-e826-470a-8413-bc736b2d9130\">\n",
|
524 |
+
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3ffda883-e826-470a-8413-bc736b2d9130')\"\n",
|
525 |
+
" title=\"Suggest charts\"\n",
|
526 |
+
" style=\"display:none;\">\n",
|
527 |
+
"\n",
|
528 |
+
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
529 |
+
" width=\"24px\">\n",
|
530 |
+
" <g>\n",
|
531 |
+
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
|
532 |
+
" </g>\n",
|
533 |
+
"</svg>\n",
|
534 |
+
" </button>\n",
|
535 |
+
"\n",
|
536 |
+
"<style>\n",
|
537 |
+
" .colab-df-quickchart {\n",
|
538 |
+
" --bg-color: #E8F0FE;\n",
|
539 |
+
" --fill-color: #1967D2;\n",
|
540 |
+
" --hover-bg-color: #E2EBFA;\n",
|
541 |
+
" --hover-fill-color: #174EA6;\n",
|
542 |
+
" --disabled-fill-color: #AAA;\n",
|
543 |
+
" --disabled-bg-color: #DDD;\n",
|
544 |
+
" }\n",
|
545 |
+
"\n",
|
546 |
+
" [theme=dark] .colab-df-quickchart {\n",
|
547 |
+
" --bg-color: #3B4455;\n",
|
548 |
+
" --fill-color: #D2E3FC;\n",
|
549 |
+
" --hover-bg-color: #434B5C;\n",
|
550 |
+
" --hover-fill-color: #FFFFFF;\n",
|
551 |
+
" --disabled-bg-color: #3B4455;\n",
|
552 |
+
" --disabled-fill-color: #666;\n",
|
553 |
+
" }\n",
|
554 |
+
"\n",
|
555 |
+
" .colab-df-quickchart {\n",
|
556 |
+
" background-color: var(--bg-color);\n",
|
557 |
+
" border: none;\n",
|
558 |
+
" border-radius: 50%;\n",
|
559 |
+
" cursor: pointer;\n",
|
560 |
+
" display: none;\n",
|
561 |
+
" fill: var(--fill-color);\n",
|
562 |
+
" height: 32px;\n",
|
563 |
+
" padding: 0;\n",
|
564 |
+
" width: 32px;\n",
|
565 |
+
" }\n",
|
566 |
+
"\n",
|
567 |
+
" .colab-df-quickchart:hover {\n",
|
568 |
+
" background-color: var(--hover-bg-color);\n",
|
569 |
+
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
570 |
+
" fill: var(--button-hover-fill-color);\n",
|
571 |
+
" }\n",
|
572 |
+
"\n",
|
573 |
+
" .colab-df-quickchart-complete:disabled,\n",
|
574 |
+
" .colab-df-quickchart-complete:disabled:hover {\n",
|
575 |
+
" background-color: var(--disabled-bg-color);\n",
|
576 |
+
" fill: var(--disabled-fill-color);\n",
|
577 |
+
" box-shadow: none;\n",
|
578 |
+
" }\n",
|
579 |
+
"\n",
|
580 |
+
" .colab-df-spinner {\n",
|
581 |
+
" border: 2px solid var(--fill-color);\n",
|
582 |
+
" border-color: transparent;\n",
|
583 |
+
" border-bottom-color: var(--fill-color);\n",
|
584 |
+
" animation:\n",
|
585 |
+
" spin 1s steps(1) infinite;\n",
|
586 |
+
" }\n",
|
587 |
+
"\n",
|
588 |
+
" @keyframes spin {\n",
|
589 |
+
" 0% {\n",
|
590 |
+
" border-color: transparent;\n",
|
591 |
+
" border-bottom-color: var(--fill-color);\n",
|
592 |
+
" border-left-color: var(--fill-color);\n",
|
593 |
+
" }\n",
|
594 |
+
" 20% {\n",
|
595 |
+
" border-color: transparent;\n",
|
596 |
+
" border-left-color: var(--fill-color);\n",
|
597 |
+
" border-top-color: var(--fill-color);\n",
|
598 |
+
" }\n",
|
599 |
+
" 30% {\n",
|
600 |
+
" border-color: transparent;\n",
|
601 |
+
" border-left-color: var(--fill-color);\n",
|
602 |
+
" border-top-color: var(--fill-color);\n",
|
603 |
+
" border-right-color: var(--fill-color);\n",
|
604 |
+
" }\n",
|
605 |
+
" 40% {\n",
|
606 |
+
" border-color: transparent;\n",
|
607 |
+
" border-right-color: var(--fill-color);\n",
|
608 |
+
" border-top-color: var(--fill-color);\n",
|
609 |
+
" }\n",
|
610 |
+
" 60% {\n",
|
611 |
+
" border-color: transparent;\n",
|
612 |
+
" border-right-color: var(--fill-color);\n",
|
613 |
+
" }\n",
|
614 |
+
" 80% {\n",
|
615 |
+
" border-color: transparent;\n",
|
616 |
+
" border-right-color: var(--fill-color);\n",
|
617 |
+
" border-bottom-color: var(--fill-color);\n",
|
618 |
+
" }\n",
|
619 |
+
" 90% {\n",
|
620 |
+
" border-color: transparent;\n",
|
621 |
+
" border-bottom-color: var(--fill-color);\n",
|
622 |
+
" }\n",
|
623 |
+
" }\n",
|
624 |
+
"</style>\n",
|
625 |
+
"\n",
|
626 |
+
" <script>\n",
|
627 |
+
" async function quickchart(key) {\n",
|
628 |
+
" const quickchartButtonEl =\n",
|
629 |
+
" document.querySelector('#' + key + ' button');\n",
|
630 |
+
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
|
631 |
+
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
|
632 |
+
" try {\n",
|
633 |
+
" const charts = await google.colab.kernel.invokeFunction(\n",
|
634 |
+
" 'suggestCharts', [key], {});\n",
|
635 |
+
" } catch (error) {\n",
|
636 |
+
" console.error('Error during call to suggestCharts:', error);\n",
|
637 |
+
" }\n",
|
638 |
+
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
|
639 |
+
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
|
640 |
+
" }\n",
|
641 |
+
" (() => {\n",
|
642 |
+
" let quickchartButtonEl =\n",
|
643 |
+
" document.querySelector('#df-3ffda883-e826-470a-8413-bc736b2d9130 button');\n",
|
644 |
+
" quickchartButtonEl.style.display =\n",
|
645 |
+
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
646 |
+
" })();\n",
|
647 |
+
" </script>\n",
|
648 |
+
"</div>\n",
|
649 |
+
"\n",
|
650 |
+
" </div>\n",
|
651 |
+
" </div>\n"
|
652 |
+
],
|
653 |
+
"application/vnd.google.colaboratory.intrinsic+json": {
|
654 |
+
"type": "dataframe",
|
655 |
+
"variable_name": "df",
|
656 |
+
"summary": "{\n \"name\": \"df\",\n \"rows\": 5063,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4854,\n \"samples\": [\n \"I Wish I Had A Girl\",\n \"Jump [Jacques Lu Cont Edit]\",\n \"Mulin' Around\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"release\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4187,\n \"samples\": [\n \"Le Bordel Magnifique\",\n \"Charlotte's Web (OST)\",\n \"X.O. Experience\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"artist_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2461,\n \"samples\": [\n \"Lee Ritenour\",\n \"Pennywise\",\n \"Anneli Drecker\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"duration\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 107.73289375974717,\n \"min\": 1.04444,\n \"max\": 1815.2224,\n \"num_unique_values\": 3939,\n \"samples\": [\n 294.24281,\n 240.79628,\n 115.53914\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"artist_familiarity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.14886096792686204,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 2474,\n \"samples\": [\n 0.787098355481,\n 0.481771820142,\n 0.374024633035\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"artist_hotttnesss\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.1347303774485448,\n \"min\": 0.0,\n \"max\": 1.08250255673,\n \"num_unique_values\": 2398,\n \"samples\": [\n 0.376018761952,\n 0.355667956383,\n 0.289970666912\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"year\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 917,\n \"min\": 0,\n \"max\": 2010,\n \"num_unique_values\": 69,\n \"samples\": [\n 1979,\n 0,\n 1965\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"listeners\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 150513,\n \"min\": 0,\n \"max\": 2451482,\n \"num_unique_values\": 3914,\n \"samples\": [\n 781546,\n 6216,\n 396579\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"playcount\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1115103,\n \"min\": 0,\n \"max\": 23182516,\n \"num_unique_values\": 4422,\n \"samples\": [\n 62736,\n 1305,\n 17033\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"tags\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4583,\n \"samples\": [\n \"dance, 90s, trance, House, jungle\",\n \"country, favorite songs, classic country, linedance, Martina McBride\",\n \"90s, heavy metal, thrash metal, metal, punk\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
|
657 |
+
}
|
658 |
+
},
|
659 |
+
"metadata": {},
|
660 |
+
"execution_count": 2
|
661 |
+
}
|
662 |
+
]
|
663 |
+
},
|
664 |
+
{
|
665 |
+
"cell_type": "code",
|
666 |
+
"source": [
|
667 |
+
"# Display basic information about the dataset\n",
|
668 |
+
"print(df.info())\n",
|
669 |
+
"\n",
|
670 |
+
"# Display summary statistics for numerical columns\n",
|
671 |
+
"print(df.describe())\n",
|
672 |
+
"\n",
|
673 |
+
"# Display unique values for categorical columns\n",
|
674 |
+
"print(\"Unique values in 'title':\", df['title'].nunique())\n",
|
675 |
+
"print(\"Unique values in 'artist_name':\", df['artist_name'].nunique())\n",
|
676 |
+
"print(\"Unique values in 'tags':\", df['tags'].nunique())"
|
677 |
+
],
|
678 |
+
"metadata": {
|
679 |
+
"colab": {
|
680 |
+
"base_uri": "https://localhost:8080/"
|
681 |
+
},
|
682 |
+
"id": "b_sSacbdHcn6",
|
683 |
+
"outputId": "f745b028-fd97-4b19-b9f0-9e041621e5d3"
|
684 |
+
},
|
685 |
+
"execution_count": null,
|
686 |
+
"outputs": [
|
687 |
+
{
|
688 |
+
"output_type": "stream",
|
689 |
+
"name": "stdout",
|
690 |
+
"text": [
|
691 |
+
"<class 'pandas.core.frame.DataFrame'>\n",
|
692 |
+
"Index: 5063 entries, 0 to 9530\n",
|
693 |
+
"Data columns (total 10 columns):\n",
|
694 |
+
" # Column Non-Null Count Dtype \n",
|
695 |
+
"--- ------ -------------- ----- \n",
|
696 |
+
" 0 title 5063 non-null object \n",
|
697 |
+
" 1 release 5063 non-null object \n",
|
698 |
+
" 2 artist_name 5063 non-null object \n",
|
699 |
+
" 3 duration 5063 non-null float64\n",
|
700 |
+
" 4 artist_familiarity 5063 non-null float64\n",
|
701 |
+
" 5 artist_hotttnesss 5063 non-null float64\n",
|
702 |
+
" 6 year 5063 non-null int64 \n",
|
703 |
+
" 7 listeners 5063 non-null int64 \n",
|
704 |
+
" 8 playcount 5063 non-null int64 \n",
|
705 |
+
" 9 tags 5063 non-null object \n",
|
706 |
+
"dtypes: float64(3), int64(3), object(4)\n",
|
707 |
+
"memory usage: 435.1+ KB\n",
|
708 |
+
"None\n",
|
709 |
+
" duration artist_familiarity artist_hotttnesss year \\\n",
|
710 |
+
"count 5063.000000 5063.000000 5063.000000 5063.000000 \n",
|
711 |
+
"mean 243.156073 0.626861 0.439664 1392.483705 \n",
|
712 |
+
"std 107.732894 0.148861 0.134730 917.360336 \n",
|
713 |
+
"min 1.044440 0.000000 0.000000 0.000000 \n",
|
714 |
+
"25% 183.535870 0.527033 0.363132 0.000000 \n",
|
715 |
+
"50% 229.145670 0.619531 0.417819 1993.000000 \n",
|
716 |
+
"75% 280.920365 0.731184 0.510325 2004.000000 \n",
|
717 |
+
"max 1815.222400 1.000000 1.082503 2010.000000 \n",
|
718 |
+
"\n",
|
719 |
+
" listeners playcount \n",
|
720 |
+
"count 5.063000e+03 5.063000e+03 \n",
|
721 |
+
"mean 4.526352e+04 2.622274e+05 \n",
|
722 |
+
"std 1.505135e+05 1.115104e+06 \n",
|
723 |
+
"min 0.000000e+00 0.000000e+00 \n",
|
724 |
+
"25% 7.545000e+02 1.894500e+03 \n",
|
725 |
+
"50% 3.387000e+03 9.439000e+03 \n",
|
726 |
+
"75% 1.787350e+04 6.269500e+04 \n",
|
727 |
+
"max 2.451482e+06 2.318252e+07 \n",
|
728 |
+
"Unique values in 'title': 4854\n",
|
729 |
+
"Unique values in 'artist_name': 2461\n",
|
730 |
+
"Unique values in 'tags': 4583\n"
|
731 |
+
]
|
732 |
+
}
|
733 |
+
]
|
734 |
+
},
|
735 |
+
{
|
736 |
+
"cell_type": "markdown",
|
737 |
+
"source": [
|
738 |
+
"# **Preprocessing**"
|
739 |
+
],
|
740 |
+
"metadata": {
|
741 |
+
"id": "wPVFDtk9g9ox"
|
742 |
+
}
|
743 |
+
},
|
744 |
+
{
|
745 |
+
"cell_type": "code",
|
746 |
+
"source": [
|
747 |
+
"import pandas as pd\n",
|
748 |
+
"from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
|
749 |
+
"import joblib\n",
|
750 |
+
"import re\n",
|
751 |
+
"\n",
|
752 |
+
"# Function to clean tags and artist names\n",
|
753 |
+
"def clean_text(text):\n",
|
754 |
+
" # Convert to lowercase\n",
|
755 |
+
" text = text.lower()\n",
|
756 |
+
" # Remove special characters and digits\n",
|
757 |
+
" text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
|
758 |
+
" # Remove extra white spaces\n",
|
759 |
+
" text = re.sub(r'\\s+', ' ', text).strip()\n",
|
760 |
+
" return text\n",
|
761 |
+
"\n",
|
762 |
+
"# Clean 'tags' and 'artist_name' columns\n",
|
763 |
+
"df['tags'] = df['tags'].apply(clean_text)\n",
|
764 |
+
"df['artist_name'] = df['artist_name'].apply(clean_text)\n",
|
765 |
+
"\n",
|
766 |
+
"def label_encode_data(df):\n",
|
767 |
+
" df = df.copy(deep=True)\n",
|
768 |
+
" label_encoders = {}\n",
|
769 |
+
" unknown_label = 'unknown' # Define an unknown label\n",
|
770 |
+
"\n",
|
771 |
+
" for column in ['tags', 'title', 'artist_name']:\n",
|
772 |
+
" le = LabelEncoder()\n",
|
773 |
+
" unique_categories = df[column].unique().tolist()\n",
|
774 |
+
" unique_categories.append(unknown_label)\n",
|
775 |
+
" le.fit(unique_categories)\n",
|
776 |
+
" df[column] = le.transform(df[column].astype(str))\n",
|
777 |
+
" label_encoders[column] = le\n",
|
778 |
+
"\n",
|
779 |
+
" return df, label_encoders\n",
|
780 |
+
"\n",
|
781 |
+
"# Normalize numerical features\n",
|
782 |
+
"scaler = MinMaxScaler()\n",
|
783 |
+
"df[['listeners', 'playcount']] = scaler.fit_transform(df[['listeners', 'playcount']])\n",
|
784 |
+
"\n",
|
785 |
+
"# Label encode categorical features\n",
|
786 |
+
"df_scaled, label_encoders = label_encode_data(df)\n",
|
787 |
+
"\n",
|
788 |
+
"# Save the encoders and scaler\n",
|
789 |
+
"joblib.dump(label_encoders, \"/content/new_label_encoders.joblib\")\n",
|
790 |
+
"joblib.dump(scaler, \"/content/new_scaler.joblib\")\n",
|
791 |
+
"\n",
|
792 |
+
"print(\"Label encoders and scaler saved successfully.\")\n"
|
793 |
+
],
|
794 |
+
"metadata": {
|
795 |
+
"colab": {
|
796 |
+
"base_uri": "https://localhost:8080/"
|
797 |
+
},
|
798 |
+
"id": "3fsU1IvylyZg",
|
799 |
+
"outputId": "c2ba3adc-c077-454a-94de-ca9bb0ba4807"
|
800 |
+
},
|
801 |
+
"execution_count": null,
|
802 |
+
"outputs": [
|
803 |
+
{
|
804 |
+
"output_type": "stream",
|
805 |
+
"name": "stdout",
|
806 |
+
"text": [
|
807 |
+
"Label encoders and scaler saved successfully.\n"
|
808 |
+
]
|
809 |
+
}
|
810 |
+
]
|
811 |
+
},
|
812 |
+
{
|
813 |
+
"cell_type": "code",
|
814 |
+
"source": [
|
815 |
+
"from sklearn.model_selection import train_test_split\n",
|
816 |
+
"\n",
|
817 |
+
"# Split data into features and target\n",
|
818 |
+
"X = df_scaled[['tags', 'artist_name']]\n",
|
819 |
+
"y = df_scaled['title']\n",
|
820 |
+
"\n",
|
821 |
+
"# Split the dataset into training and testing sets\n",
|
822 |
+
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
|
823 |
+
"print(\"Data split into training and testing sets.\")\n",
|
824 |
+
"\n",
|
825 |
+
"# Number of unique titles\n",
|
826 |
+
"num_unique_titles = len(label_encoders['title'].classes_)\n",
|
827 |
+
"\n",
|
828 |
+
"# Check for out-of-bounds indices in y_train and y_test\n",
|
829 |
+
"print(\"Maximum value in y_train:\", y_train.max())\n",
|
830 |
+
"print(\"Maximum value in y_test:\", y_test.max())\n",
|
831 |
+
"print(\"Number of unique titles:\", num_unique_titles)\n",
|
832 |
+
"\n",
|
833 |
+
"# If any out-of-bounds values are found, print them\n",
|
834 |
+
"out_of_bounds_train = y_train[y_train >= num_unique_titles]\n",
|
835 |
+
"out_of_bounds_test = y_test[y_test >= num_unique_titles]\n",
|
836 |
+
"\n",
|
837 |
+
"if not out_of_bounds_train.empty:\n",
|
838 |
+
" print(\"Out-of-bounds values in y_train:\", out_of_bounds_train)\n",
|
839 |
+
"if not out_of_bounds_test.empty:\n",
|
840 |
+
" print(\"Out-of-bounds values in y_test:\", out_of_bounds_test)\n",
|
841 |
+
"\n",
|
842 |
+
"# Fix out-of-bounds values by setting them to a valid index\n",
|
843 |
+
"y_train = y_train.clip(upper=num_unique_titles - 1)\n",
|
844 |
+
"y_test = y_test.clip(upper=num_unique_titles - 1)\n",
|
845 |
+
"\n",
|
846 |
+
"# Print the maximum values after clipping\n",
|
847 |
+
"print(\"Maximum value in y_train after clipping:\", y_train.max())\n",
|
848 |
+
"print(\"Maximum value in y_test after clipping:\", y_test.max())\n"
|
849 |
+
],
|
850 |
+
"metadata": {
|
851 |
+
"colab": {
|
852 |
+
"base_uri": "https://localhost:8080/"
|
853 |
+
},
|
854 |
+
"id": "JBWZWp_8Jr82",
|
855 |
+
"outputId": "73a312c1-3615-4a87-965b-c2fc41fc50e7"
|
856 |
+
},
|
857 |
+
"execution_count": null,
|
858 |
+
"outputs": [
|
859 |
+
{
|
860 |
+
"output_type": "stream",
|
861 |
+
"name": "stdout",
|
862 |
+
"text": [
|
863 |
+
"Data split into training and testing sets.\n",
|
864 |
+
"Maximum value in y_train: 4854\n",
|
865 |
+
"Maximum value in y_test: 4850\n",
|
866 |
+
"Number of unique titles: 4855\n",
|
867 |
+
"Maximum value in y_train after clipping: 4854\n",
|
868 |
+
"Maximum value in y_test after clipping: 4850\n"
|
869 |
+
]
|
870 |
+
}
|
871 |
+
]
|
872 |
+
},
|
873 |
+
{
|
874 |
+
"cell_type": "markdown",
|
875 |
+
"source": [
|
876 |
+
"# **Training**"
|
877 |
+
],
|
878 |
+
"metadata": {
|
879 |
+
"id": "syYhdUbxgA-K"
|
880 |
+
}
|
881 |
+
},
|
882 |
+
{
|
883 |
+
"cell_type": "code",
|
884 |
+
"source": [
|
885 |
+
"import torch\n",
|
886 |
+
"import torch.nn as nn\n",
|
887 |
+
"import torch.optim as optim\n",
|
888 |
+
"from torch.utils.data import DataLoader\n",
|
889 |
+
"import numpy as np\n",
|
890 |
+
"\n",
|
891 |
+
"# Define the neural network model with Dropout and Batch Normalization\n",
|
892 |
+
"class ImprovedSongRecommender(nn.Module):\n",
|
893 |
+
" def __init__(self, input_size, num_titles):\n",
|
894 |
+
" super(ImprovedSongRecommender, self).__init__()\n",
|
895 |
+
" self.fc1 = nn.Linear(input_size, 128)\n",
|
896 |
+
" self.bn1 = nn.BatchNorm1d(128)\n",
|
897 |
+
" self.fc2 = nn.Linear(128, 256)\n",
|
898 |
+
" self.bn2 = nn.BatchNorm1d(256)\n",
|
899 |
+
" self.fc3 = nn.Linear(256, 128)\n",
|
900 |
+
" self.bn3 = nn.BatchNorm1d(128)\n",
|
901 |
+
" self.output = nn.Linear(128, num_titles)\n",
|
902 |
+
" self.dropout = nn.Dropout(0.5)\n",
|
903 |
+
"\n",
|
904 |
+
" def forward(self, x):\n",
|
905 |
+
" x = torch.relu(self.bn1(self.fc1(x)))\n",
|
906 |
+
" x = self.dropout(x)\n",
|
907 |
+
" x = torch.relu(self.bn2(self.fc2(x)))\n",
|
908 |
+
" x = self.dropout(x)\n",
|
909 |
+
" x = torch.relu(self.bn3(self.fc3(x)))\n",
|
910 |
+
" x = self.dropout(x)\n",
|
911 |
+
" x = self.output(x)\n",
|
912 |
+
" return x\n",
|
913 |
+
"\n",
|
914 |
+
"# Adjusting input size for the model\n",
|
915 |
+
"input_size = X_train.shape[1] # Number of features in the input\n",
|
916 |
+
"num_unique_titles = len(label_encoders['title'].classes_) # Number of unique titles including 'unknown'\n",
|
917 |
+
"\n",
|
918 |
+
"# Initialize the model with the correct input size and output size\n",
|
919 |
+
"model = ImprovedSongRecommender(input_size, num_unique_titles)\n",
|
920 |
+
"\n",
|
921 |
+
"# Initialize the optimizer and loss function\n",
|
922 |
+
"optimizer = optim.AdamW(model.parameters(), lr=0.001, weight_decay=0.01)\n",
|
923 |
+
"criterion = nn.CrossEntropyLoss()\n",
|
924 |
+
"\n",
|
925 |
+
"# Use a learning rate scheduler\n",
|
926 |
+
"scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)\n",
|
927 |
+
"\n",
|
928 |
+
"# Early stopping parameters\n",
|
929 |
+
"patience = 3\n",
|
930 |
+
"min_delta = 0.01\n",
|
931 |
+
"best_val_loss = np.inf\n",
|
932 |
+
"patience_counter = 0\n",
|
933 |
+
"\n",
|
934 |
+
"# Function to train the model\n",
|
935 |
+
"def train_model(model, X_train, y_train, X_test, y_test):\n",
|
936 |
+
" global best_val_loss, patience_counter\n",
|
937 |
+
" train_loader = DataLoader(list(zip(X_train.values.astype(float), y_train)), batch_size=10, shuffle=True)\n",
|
938 |
+
" test_loader = DataLoader(list(zip(X_test.values.astype(float), y_test)), batch_size=10, shuffle=False)\n",
|
939 |
+
"\n",
|
940 |
+
" model.train()\n",
|
941 |
+
" for epoch in range(20): # Increase the number of epochs\n",
|
942 |
+
" train_loss = 0\n",
|
943 |
+
" for features, labels in train_loader:\n",
|
944 |
+
" optimizer.zero_grad()\n",
|
945 |
+
" outputs = model(features.float())\n",
|
946 |
+
" loss = criterion(outputs, labels.long())\n",
|
947 |
+
" loss.backward()\n",
|
948 |
+
" optimizer.step()\n",
|
949 |
+
" train_loss += loss.item()\n",
|
950 |
+
"\n",
|
951 |
+
" # Step the scheduler\n",
|
952 |
+
" scheduler.step()\n",
|
953 |
+
"\n",
|
954 |
+
" # Validation phase\n",
|
955 |
+
" model.eval()\n",
|
956 |
+
" validation_loss = 0\n",
|
957 |
+
" with torch.no_grad():\n",
|
958 |
+
" for features, labels in test_loader:\n",
|
959 |
+
" outputs = model(features.float())\n",
|
960 |
+
" loss = criterion(outputs, labels.long())\n",
|
961 |
+
" validation_loss += loss.item()\n",
|
962 |
+
"\n",
|
963 |
+
" avg_val_loss = validation_loss / len(test_loader)\n",
|
964 |
+
" print(f'Epoch {epoch+1}, Training Loss: {train_loss / len(train_loader)}, Validation Loss: {avg_val_loss}')\n",
|
965 |
+
"\n",
|
966 |
+
" # Early stopping\n",
|
967 |
+
" if avg_val_loss < best_val_loss - min_delta:\n",
|
968 |
+
" best_val_loss = avg_val_loss\n",
|
969 |
+
" patience_counter = 0\n",
|
970 |
+
" else:\n",
|
971 |
+
" patience_counter += 1\n",
|
972 |
+
" if patience_counter >= patience:\n",
|
973 |
+
" print(\"Early stopping triggered\")\n",
|
974 |
+
" break\n",
|
975 |
+
"\n",
|
976 |
+
"# Train the model\n",
|
977 |
+
"train_model(model, X_train, y_train, X_test, y_test)\n",
|
978 |
+
"\n",
|
979 |
+
"# Save the trained model\n",
|
980 |
+
"model_path = '/content/improved_model.pth'\n",
|
981 |
+
"torch.save(model.state_dict(), model_path)\n",
|
982 |
+
"\n",
|
983 |
+
"print(\"Improved model trained and saved successfully.\")\n"
|
984 |
+
],
|
985 |
+
"metadata": {
|
986 |
+
"colab": {
|
987 |
+
"base_uri": "https://localhost:8080/"
|
988 |
+
},
|
989 |
+
"id": "aaR1IGymKQq2",
|
990 |
+
"outputId": "9e5115a5-1a75-4672-a0b3-4fdd314e1a79"
|
991 |
+
},
|
992 |
+
"execution_count": null,
|
993 |
+
"outputs": [
|
994 |
+
{
|
995 |
+
"output_type": "stream",
|
996 |
+
"name": "stdout",
|
997 |
+
"text": [
|
998 |
+
"Epoch 1, Training Loss: 8.921830113728841, Validation Loss: 8.836441385979747\n",
|
999 |
+
"Epoch 2, Training Loss: 8.331391870239635, Validation Loss: 9.148561271966672\n",
|
1000 |
+
"Epoch 3, Training Loss: 7.494005516429007, Validation Loss: 10.484928570541681\n",
|
1001 |
+
"Epoch 4, Training Loss: 6.704833826606657, Validation Loss: 11.745069999320835\n",
|
1002 |
+
"Early stopping triggered\n",
|
1003 |
+
"Improved model trained and saved successfully.\n"
|
1004 |
+
]
|
1005 |
+
}
|
1006 |
+
]
|
1007 |
+
},
|
1008 |
+
{
|
1009 |
+
"cell_type": "markdown",
|
1010 |
+
"source": [
|
1011 |
+
"# **Testing**"
|
1012 |
+
],
|
1013 |
+
"metadata": {
|
1014 |
+
"id": "g4hJVlNXf5Vu"
|
1015 |
+
}
|
1016 |
+
},
|
1017 |
+
{
|
1018 |
+
"cell_type": "code",
|
1019 |
+
"source": [
|
1020 |
+
"import torch\n",
|
1021 |
+
"from joblib import load\n",
|
1022 |
+
"\n",
|
1023 |
+
"# Define the same neural network model\n",
|
1024 |
+
"class ImprovedSongRecommender(nn.Module):\n",
|
1025 |
+
" def __init__(self, input_size, num_titles):\n",
|
1026 |
+
" super(ImprovedSongRecommender, self).__init__()\n",
|
1027 |
+
" self.fc1 = nn.Linear(input_size, 128)\n",
|
1028 |
+
" self.bn1 = nn.BatchNorm1d(128)\n",
|
1029 |
+
" self.fc2 = nn.Linear(128, 256)\n",
|
1030 |
+
" self.bn2 = nn.BatchNorm1d(256)\n",
|
1031 |
+
" self.fc3 = nn.Linear(256, 128)\n",
|
1032 |
+
" self.bn3 = nn.BatchNorm1d(128)\n",
|
1033 |
+
" self.output = nn.Linear(128, num_titles)\n",
|
1034 |
+
" self.dropout = nn.Dropout(0.5)\n",
|
1035 |
+
"\n",
|
1036 |
+
" def forward(self, x):\n",
|
1037 |
+
" x = torch.relu(self.bn1(self.fc1(x)))\n",
|
1038 |
+
" x = self.dropout(x)\n",
|
1039 |
+
" x = torch.relu(self.bn2(self.fc2(x)))\n",
|
1040 |
+
" x = self.dropout(x)\n",
|
1041 |
+
" x = torch.relu(self.bn3(self.fc3(x)))\n",
|
1042 |
+
" x = self.dropout(x)\n",
|
1043 |
+
" x = self.output(x)\n",
|
1044 |
+
" return x\n",
|
1045 |
+
"\n",
|
1046 |
+
"# Load the trained model\n",
|
1047 |
+
"model_path = '/content/improved_model.pth'\n",
|
1048 |
+
"num_unique_titles = 4855 # Update this to match your dataset\n",
|
1049 |
+
"\n",
|
1050 |
+
"model = ImprovedSongRecommender(input_size=2, num_titles=num_unique_titles) # Adjust input size accordingly\n",
|
1051 |
+
"model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))\n",
|
1052 |
+
"model.eval()\n",
|
1053 |
+
"\n",
|
1054 |
+
"# Load the label encoders and scaler\n",
|
1055 |
+
"label_encoders_path = '/content/new_label_encoders.joblib'\n",
|
1056 |
+
"scaler_path = '/content/new_scaler.joblib'\n",
|
1057 |
+
"\n",
|
1058 |
+
"label_encoders = load(label_encoders_path)\n",
|
1059 |
+
"scaler = load(scaler_path)\n",
|
1060 |
+
"\n",
|
1061 |
+
"# Create a mapping from encoded indices to actual song titles\n",
|
1062 |
+
"index_to_song_title = {index: title for index, title in enumerate(label_encoders['title'].classes_)}\n",
|
1063 |
+
"\n",
|
1064 |
+
"def encode_input(tags, artist_name):\n",
|
1065 |
+
" tags = tags.strip().replace('\\n', '')\n",
|
1066 |
+
" artist_name = artist_name.strip().replace('\\n', '')\n",
|
1067 |
+
"\n",
|
1068 |
+
" try:\n",
|
1069 |
+
" encoded_tags = label_encoders['tags'].transform([tags])[0]\n",
|
1070 |
+
" except ValueError:\n",
|
1071 |
+
" encoded_tags = label_encoders['tags'].transform(['unknown'])[0]\n",
|
1072 |
+
"\n",
|
1073 |
+
" try:\n",
|
1074 |
+
" encoded_artist = label_encoders['artist_name'].transform([artist_name])[0]\n",
|
1075 |
+
" except ValueError:\n",
|
1076 |
+
" encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]\n",
|
1077 |
+
"\n",
|
1078 |
+
" return [encoded_tags, encoded_artist]\n",
|
1079 |
+
"\n",
|
1080 |
+
"def recommend_songs(tags, artist_name):\n",
|
1081 |
+
" encoded_input = encode_input(tags, artist_name)\n",
|
1082 |
+
" input_tensor = torch.tensor([encoded_input]).float()\n",
|
1083 |
+
"\n",
|
1084 |
+
" with torch.no_grad():\n",
|
1085 |
+
" output = model(input_tensor)\n",
|
1086 |
+
"\n",
|
1087 |
+
" recommendations_indices = torch.topk(output, 5).indices.squeeze().tolist()\n",
|
1088 |
+
" recommendations = [index_to_song_title.get(idx, \"Unknown song\") for idx in recommendations_indices]\n",
|
1089 |
+
"\n",
|
1090 |
+
" return recommendations\n",
|
1091 |
+
"\n",
|
1092 |
+
"# Test the recommendation function\n",
|
1093 |
+
"tags = \"rock\"\n",
|
1094 |
+
"artist_name = \"The Beatles\"\n",
|
1095 |
+
"\n",
|
1096 |
+
"recommendations = recommend_songs(tags, artist_name)\n",
|
1097 |
+
"print(\"Recommendations:\", recommendations)\n"
|
1098 |
+
],
|
1099 |
+
"metadata": {
|
1100 |
+
"colab": {
|
1101 |
+
"base_uri": "https://localhost:8080/"
|
1102 |
+
},
|
1103 |
+
"id": "KwqV-HnCOvtz",
|
1104 |
+
"outputId": "d412ce92-3ab8-4f3d-df83-22ef9e857203"
|
1105 |
+
},
|
1106 |
+
"execution_count": null,
|
1107 |
+
"outputs": [
|
1108 |
+
{
|
1109 |
+
"output_type": "stream",
|
1110 |
+
"name": "stdout",
|
1111 |
+
"text": [
|
1112 |
+
"Recommendations: ['Betrayal Is A Symptom', 'The Earth Will Shake', 'Saturday', 'Firehouse Rock', 'Breathe Easy']\n"
|
1113 |
+
]
|
1114 |
+
}
|
1115 |
+
]
|
1116 |
+
},
|
1117 |
+
{
|
1118 |
+
"cell_type": "code",
|
1119 |
+
"source": [
|
1120 |
+
"import torch\n",
|
1121 |
+
"from joblib import load\n",
|
1122 |
+
"\n",
|
1123 |
+
"# Define the same neural network model\n",
|
1124 |
+
"class ImprovedSongRecommender(nn.Module):\n",
|
1125 |
+
" def __init__(self, input_size, num_titles):\n",
|
1126 |
+
" super(ImprovedSongRecommender, self).__init__()\n",
|
1127 |
+
" self.fc1 = nn.Linear(input_size, 128)\n",
|
1128 |
+
" self.bn1 = nn.BatchNorm1d(128)\n",
|
1129 |
+
" self.fc2 = nn.Linear(128, 256)\n",
|
1130 |
+
" self.bn2 = nn.BatchNorm1d(256)\n",
|
1131 |
+
" self.fc3 = nn.Linear(256, 128)\n",
|
1132 |
+
" self.bn3 = nn.BatchNorm1d(128)\n",
|
1133 |
+
" self.output = nn.Linear(128, num_titles)\n",
|
1134 |
+
" self.dropout = nn.Dropout(0.5)\n",
|
1135 |
+
"\n",
|
1136 |
+
" def forward(self, x):\n",
|
1137 |
+
" x = torch.relu(self.bn1(self.fc1(x)))\n",
|
1138 |
+
" x = self.dropout(x)\n",
|
1139 |
+
" x = torch.relu(self.bn2(self.fc2(x)))\n",
|
1140 |
+
" x = self.dropout(x)\n",
|
1141 |
+
" x = torch.relu(self.bn3(self.fc3(x)))\n",
|
1142 |
+
" x = self.dropout(x)\n",
|
1143 |
+
" x = self.output(x)\n",
|
1144 |
+
" return x\n",
|
1145 |
+
"\n",
|
1146 |
+
"# Load the trained model\n",
|
1147 |
+
"model_path = '/content/improved_model.pth'\n",
|
1148 |
+
"num_unique_titles = 4855 # Update this to match your dataset\n",
|
1149 |
+
"\n",
|
1150 |
+
"model = ImprovedSongRecommender(input_size=2, num_titles=num_unique_titles) # Adjust input size accordingly\n",
|
1151 |
+
"model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))\n",
|
1152 |
+
"model.eval()\n",
|
1153 |
+
"\n",
|
1154 |
+
"# Load the label encoders and scaler\n",
|
1155 |
+
"label_encoders_path = '/content/new_label_encoders.joblib'\n",
|
1156 |
+
"scaler_path = '/content/new_scaler.joblib'\n",
|
1157 |
+
"\n",
|
1158 |
+
"label_encoders = load(label_encoders_path)\n",
|
1159 |
+
"scaler = load(scaler_path)\n",
|
1160 |
+
"\n",
|
1161 |
+
"# Create a mapping from encoded indices to actual song titles\n",
|
1162 |
+
"index_to_song_title = {index: title for index, title in enumerate(label_encoders['title'].classes_)}\n",
|
1163 |
+
"\n",
|
1164 |
+
"def encode_input(tags, artist_name):\n",
|
1165 |
+
" tags = tags.strip().replace('\\n', '')\n",
|
1166 |
+
" artist_name = artist_name.strip().replace('\\n', '')\n",
|
1167 |
+
"\n",
|
1168 |
+
" try:\n",
|
1169 |
+
" encoded_tags = label_encoders['tags'].transform([tags])[0]\n",
|
1170 |
+
" except ValueError:\n",
|
1171 |
+
" encoded_tags = label_encoders['tags'].transform(['unknown'])[0]\n",
|
1172 |
+
"\n",
|
1173 |
+
" try:\n",
|
1174 |
+
" encoded_artist = label_encoders['artist_name'].transform([artist_name])[0]\n",
|
1175 |
+
" except ValueError:\n",
|
1176 |
+
" encoded_artist = label_encoders['artist_name'].transform(['unknown'])[0]\n",
|
1177 |
+
"\n",
|
1178 |
+
" return [encoded_tags, encoded_artist]\n",
|
1179 |
+
"\n",
|
1180 |
+
"def recommend_songs(tags, artist_name):\n",
|
1181 |
+
" encoded_input = encode_input(tags, artist_name)\n",
|
1182 |
+
" input_tensor = torch.tensor([encoded_input]).float()\n",
|
1183 |
+
"\n",
|
1184 |
+
" with torch.no_grad():\n",
|
1185 |
+
" output = model(input_tensor)\n",
|
1186 |
+
"\n",
|
1187 |
+
" recommendations_indices = torch.topk(output, 5).indices.squeeze().tolist()\n",
|
1188 |
+
" recommendations = [index_to_song_title.get(idx, \"Unknown song\") for idx in recommendations_indices]\n",
|
1189 |
+
"\n",
|
1190 |
+
" return recommendations\n",
|
1191 |
+
"\n",
|
1192 |
+
"# Test the recommendation function with new inputs\n",
|
1193 |
+
"tags = \"pop\"\n",
|
1194 |
+
"artist_name = \"Adele\"\n",
|
1195 |
+
"\n",
|
1196 |
+
"recommendations = recommend_songs(tags, artist_name)\n",
|
1197 |
+
"print(\"Recommendations:\", recommendations)\n",
|
1198 |
+
"\n",
|
1199 |
+
"# Test with another set of inputs\n",
|
1200 |
+
"tags = \"jazz\"\n",
|
1201 |
+
"artist_name = \"Miles Davis\"\n",
|
1202 |
+
"\n",
|
1203 |
+
"recommendations = recommend_songs(tags, artist_name)\n",
|
1204 |
+
"print(\"Recommendations:\", recommendations)\n"
|
1205 |
+
],
|
1206 |
+
"metadata": {
|
1207 |
+
"colab": {
|
1208 |
+
"base_uri": "https://localhost:8080/"
|
1209 |
+
},
|
1210 |
+
"id": "3HzLKv5mPxOv",
|
1211 |
+
"outputId": "62b37d04-4857-44fb-b5c4-8ead55db9b1a"
|
1212 |
+
},
|
1213 |
+
"execution_count": null,
|
1214 |
+
"outputs": [
|
1215 |
+
{
|
1216 |
+
"output_type": "stream",
|
1217 |
+
"name": "stdout",
|
1218 |
+
"text": [
|
1219 |
+
"Recommendations: ['Betrayal Is A Symptom', 'Carnival (from \"Black Orpheus\")', 'Saturday', 'The Earth Will Shake', 'Start!']\n",
|
1220 |
+
"Recommendations: ['Old Friends', 'Betrayal Is A Symptom', 'Between Love & Hate', 'Carnival (from \"Black Orpheus\")', 'Satin Doll']\n"
|
1221 |
+
]
|
1222 |
+
}
|
1223 |
+
]
|
1224 |
+
}
|
1225 |
+
]
|
1226 |
+
}
|