yukseltron
commited on
Commit
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8c7ecd7
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Parent(s):
9015aef
modify json input
Browse files- catboost_info/catboost_training.json +100 -100
- catboost_info/learn/events.out.tfevents +1 -1
- catboost_info/time_left.tsv +100 -100
- model.ipynb +519 -0
- model.pickle +3 -0
- request.py +3 -4
- server.py +24 -0
- vercel.json +1 -1
catboost_info/catboost_training.json
CHANGED
@@ -1,104 +1,104 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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5 |
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"id": "5ede69a1",
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6 |
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"metadata": {},
|
7 |
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"source": [
|
8 |
+
"# Classification Challenge using CatBoost\n",
|
9 |
+
"\n",
|
10 |
+
"## INF2179 Fall 2021\n",
|
11 |
+
"### Hamid Yuksel\n",
|
12 |
+
"\n",
|
13 |
+
"This submission uses [CatBoost](https://catboost.ai/).\n",
|
14 |
+
"CatBoost was chosen for its listed benefits, mainly in requiring less hyperparameter tuning and preprocessing of categorical and text features. It is also fast and fairly easy to set up.\n",
|
15 |
+
"\n",
|
16 |
+
"<img src=\"https://cdn.britannica.com/39/7139-050-A88818BB/Himalayan-chocolate-point.jpg\"\n",
|
17 |
+
" alt=\"Markdown Monster icon\"\n",
|
18 |
+
" style=\"float: left; margin-right: 10px;\" />\n"
|
19 |
+
]
|
20 |
+
},
|
21 |
+
{
|
22 |
+
"cell_type": "code",
|
23 |
+
"execution_count": 1,
|
24 |
+
"id": "ee82451e",
|
25 |
+
"metadata": {},
|
26 |
+
"outputs": [
|
27 |
+
{
|
28 |
+
"name": "stdout",
|
29 |
+
"output_type": "stream",
|
30 |
+
"text": [
|
31 |
+
"Requirement already satisfied: catboost in /Users/yuksel/.local/lib/python3.8/site-packages (1.0.3)\n",
|
32 |
+
"Requirement already satisfied: numpy>=1.16.0 in /Users/yuksel/opt/anaconda3/lib/python3.8/site-packages (from catboost) (1.20.1)\n",
|
33 |
+
"Requirement already satisfied: scipy in /Users/yuksel/opt/anaconda3/lib/python3.8/site-packages (from catboost) (1.8.0)\n",
|
34 |
+
"Requirement already satisfied: matplotlib in /Users/yuksel/opt/anaconda3/lib/python3.8/site-packages (from catboost) (3.4.3)\n",
|
35 |
+
"Requirement already satisfied: plotly in /Users/yuksel/.local/lib/python3.8/site-packages (from catboost) (5.3.1)\n",
|
36 |
+
"Requirement already satisfied: pandas>=0.24.0 in /Users/yuksel/opt/anaconda3/lib/python3.8/site-packages (from catboost) (1.2.4)\n",
|
37 |
+
"Requirement already satisfied: graphviz in /Users/yuksel/.local/lib/python3.8/site-packages (from catboost) (0.18)\n",
|
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+
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"source": [
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"#Installing and Importing required libraries\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.metrics import accuracy_score \n",
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"Rock 24486\n",
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"Pop 16251\n",
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"Hip Hop 9263\n",
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"unknown 5000\n",
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"Name: Genre, dtype: int64"
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"source": [
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"# Reading data\n",
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"df = pd.read_csv('data.csv')\n",
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"\n",
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"# Splitting\n",
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"training = df.head(50000)\n",
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"holdout_set = training.sample(5000, random_state=1) # pick 5000 observations randomly\n",
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"training = training.drop(holdout_set.index) # Remove holdout from training data\n",
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"testing = df.tail(5000)\n",
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"\n",
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"#Looking at counts per genre\n",
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"df['Genre'].value_counts()"
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Lyric</th>\n",
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" <th>50000</th>\n",
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" <td>Feels so good,. Feels so good,. Feels so good ...</td>\n",
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" <tr>\n",
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" <th>50001</th>\n",
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" <td>Shadow of a doubt. I heard your heart,. you he...</td>\n",
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" <th>50002</th>\n",
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" <td>Slaves. Hebrews born to serve to the pharaoh. ...</td>\n",
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" <tr>\n",
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" <th>50003</th>\n",
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" <td>You've been picked and it's over. What's the c...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50004</th>\n",
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" <td>Magic happens. But only if you are open to the...</td>\n",
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" <td>I can't believe what you did to me. Down on my...</td>\n",
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" <th>54996</th>\n",
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" <td>Have all the songs been written?. Have all the...</td>\n",
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" <th>54997</th>\n",
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" <td>Everything you do you do so right. The clothes...</td>\n",
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" <th>54999</th>\n",
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" <td>As fall rides off in the Sunset. I sweep the S...</td>\n",
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"text/plain": [
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" Lyric\n",
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"50000 Feels so good,. Feels so good,. Feels so good ...\n",
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"50001 Shadow of a doubt. I heard your heart,. you he...\n",
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"50002 Slaves. Hebrews born to serve to the pharaoh. ...\n",
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"50003 You've been picked and it's over. What's the c...\n",
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"50004 Magic happens. But only if you are open to the...\n",
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"... ...\n",
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"54995 I can't believe what you did to me. Down on my...\n",
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"54996 Have all the songs been written?. Have all the...\n",
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"54997 Everything you do you do so right. The clothes...\n",
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"54998 (trecho). (Rule Number Two. Understanding what...\n",
|
253 |
+
"54999 As fall rides off in the Sunset. I sweep the S...\n",
|
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+
"\n",
|
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+
"[5000 rows x 1 columns]"
|
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+
]
|
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+
},
|
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+
"execution_count": 3,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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+
}
|
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+
],
|
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+
"source": [
|
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+
"# Splitting training/testing set to feature (X) and labels (y)\n",
|
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+
"train_y = training.Genre\n",
|
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+
"train_X = training.drop('Genre', axis=1)\n",
|
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+
"\n",
|
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+
"test_X = testing.drop('Genre', axis=1)\n",
|
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+
"\n",
|
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+
"test_X"
|
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+
]
|
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{
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"cell_type": "code",
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"execution_count": 5,
|
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"id": "1c8eb420",
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"name": "stdout",
|
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"output_type": "stream",
|
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|
383 |
+
]
|
384 |
+
},
|
385 |
+
{
|
386 |
+
"data": {
|
387 |
+
"text/plain": [
|
388 |
+
"<catboost.core.CatBoostClassifier at 0x7fb6f45e2c70>"
|
389 |
+
]
|
390 |
+
},
|
391 |
+
"execution_count": 5,
|
392 |
+
"metadata": {},
|
393 |
+
"output_type": "execute_result"
|
394 |
+
}
|
395 |
+
],
|
396 |
+
"source": [
|
397 |
+
"# Build a classifier\n",
|
398 |
+
"text_features = ['Lyric']\n",
|
399 |
+
"\n",
|
400 |
+
"\n",
|
401 |
+
"train_dataset = Pool(data=train_X,\n",
|
402 |
+
" label=train_y,\n",
|
403 |
+
" text_features=text_features)\n",
|
404 |
+
"\n",
|
405 |
+
"model = CatBoostClassifier(iterations=100,\n",
|
406 |
+
" learning_rate=1,\n",
|
407 |
+
" depth=5,\n",
|
408 |
+
" loss_function='MultiClass')\n",
|
409 |
+
"\n",
|
410 |
+
"model.fit(train_dataset)"
|
411 |
+
]
|
412 |
+
},
|
413 |
+
{
|
414 |
+
"cell_type": "code",
|
415 |
+
"execution_count": 6,
|
416 |
+
"id": "f770a8f2",
|
417 |
+
"metadata": {},
|
418 |
+
"outputs": [
|
419 |
+
{
|
420 |
+
"name": "stdout",
|
421 |
+
"output_type": "stream",
|
422 |
+
"text": [
|
423 |
+
"0.6796\n"
|
424 |
+
]
|
425 |
+
}
|
426 |
+
],
|
427 |
+
"source": [
|
428 |
+
"# Estimate accuracy\n",
|
429 |
+
"pred = model.predict(holdout_set.drop('Genre',axis=1))\n",
|
430 |
+
"estimated_accuracy = accuracy_score(holdout_set['Genre'], pred)\n",
|
431 |
+
"print(estimated_accuracy)\n",
|
432 |
+
"pd.Series(estimated_accuracy).to_csv('ea.csv', index=False, header=False)"
|
433 |
+
]
|
434 |
+
},
|
435 |
+
{
|
436 |
+
"cell_type": "code",
|
437 |
+
"execution_count": 7,
|
438 |
+
"id": "035f58c6",
|
439 |
+
"metadata": {},
|
440 |
+
"outputs": [
|
441 |
+
{
|
442 |
+
"name": "stdout",
|
443 |
+
"output_type": "stream",
|
444 |
+
"text": [
|
445 |
+
"['Pop' 'Rock' 'Rock' ... 'Rock' 'Pop' 'Rock']\n"
|
446 |
+
]
|
447 |
+
}
|
448 |
+
],
|
449 |
+
"source": [
|
450 |
+
"# Predict testing set\n",
|
451 |
+
"pred = model.predict(test_X)\n",
|
452 |
+
"print(pred.flatten())\n",
|
453 |
+
"pred = pd.Series(pred.flatten()).to_csv('pred.csv', index=False, header=False)"
|
454 |
+
]
|
455 |
+
},
|
456 |
+
{
|
457 |
+
"cell_type": "code",
|
458 |
+
"execution_count": 8,
|
459 |
+
"id": "2137c958",
|
460 |
+
"metadata": {},
|
461 |
+
"outputs": [
|
462 |
+
{
|
463 |
+
"data": {
|
464 |
+
"text/plain": [
|
465 |
+
"(array(['Hip Hop', 'Pop', 'Rock'], dtype=object), array([ 821, 1385, 2794]))"
|
466 |
+
]
|
467 |
+
},
|
468 |
+
"execution_count": 8,
|
469 |
+
"metadata": {},
|
470 |
+
"output_type": "execute_result"
|
471 |
+
}
|
472 |
+
],
|
473 |
+
"source": [
|
474 |
+
"# to check number of instances of each genre in pred\n",
|
475 |
+
"np.unique(model.predict(test_X), return_counts=True)"
|
476 |
+
]
|
477 |
+
},
|
478 |
+
{
|
479 |
+
"cell_type": "code",
|
480 |
+
"execution_count": null,
|
481 |
+
"id": "83aa22c4",
|
482 |
+
"metadata": {},
|
483 |
+
"outputs": [],
|
484 |
+
"source": [
|
485 |
+
"import pickle\n",
|
486 |
+
"pickle.dump(model, open('model.pickle', 'wb'))"
|
487 |
+
]
|
488 |
+
},
|
489 |
+
{
|
490 |
+
"cell_type": "code",
|
491 |
+
"execution_count": null,
|
492 |
+
"id": "8c3b136d",
|
493 |
+
"metadata": {},
|
494 |
+
"outputs": [],
|
495 |
+
"source": []
|
496 |
+
}
|
497 |
+
],
|
498 |
+
"metadata": {
|
499 |
+
"kernelspec": {
|
500 |
+
"display_name": "Python 3",
|
501 |
+
"language": "python",
|
502 |
+
"name": "python3"
|
503 |
+
},
|
504 |
+
"language_info": {
|
505 |
+
"codemirror_mode": {
|
506 |
+
"name": "ipython",
|
507 |
+
"version": 3
|
508 |
+
},
|
509 |
+
"file_extension": ".py",
|
510 |
+
"mimetype": "text/x-python",
|
511 |
+
"name": "python",
|
512 |
+
"nbconvert_exporter": "python",
|
513 |
+
"pygments_lexer": "ipython3",
|
514 |
+
"version": "3.8.8"
|
515 |
+
}
|
516 |
+
},
|
517 |
+
"nbformat": 4,
|
518 |
+
"nbformat_minor": 5
|
519 |
+
}
|
model.pickle
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ff004a32317f1ed907d7aa6960746578c802f2ffff4b586b7bc73e38dd95c49f
|
3 |
+
size 7738041
|
request.py
CHANGED
@@ -1,10 +1,9 @@
|
|
1 |
import requests
|
2 |
import json
|
3 |
|
4 |
-
url = '
|
5 |
|
6 |
data = [["baby you can drive my car"]]
|
7 |
-
j_data = json.dumps(data)
|
8 |
headers = {'content-type': 'application/json', 'Accept-Charset': 'UTF-8'}
|
9 |
-
r = requests.post(url, data
|
10 |
-
print(r, r.
|
|
|
1 |
import requests
|
2 |
import json
|
3 |
|
4 |
+
url = 'http://0.0.0.0:5000/api/'
|
5 |
|
6 |
data = [["baby you can drive my car"]]
|
|
|
7 |
headers = {'content-type': 'application/json', 'Accept-Charset': 'UTF-8'}
|
8 |
+
r = requests.post(url, json={'lyric':data,}, headers=headers)
|
9 |
+
print(r, r.json())
|
server.py
ADDED
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from flask import Flask, request, redirect, url_for, flash, jsonify
|
2 |
+
import numpy as np
|
3 |
+
import pickle as p
|
4 |
+
import json
|
5 |
+
|
6 |
+
|
7 |
+
app = Flask(__name__)
|
8 |
+
|
9 |
+
@app.route("/")
|
10 |
+
def home():
|
11 |
+
return "<h1>Lyrics Classifier</h1>"
|
12 |
+
|
13 |
+
@app.route('/api/', methods=['POST'])
|
14 |
+
def makecalc():
|
15 |
+
data = request.get_json(force=True)
|
16 |
+
prediction = np.array2string(model.predict(data['lyric']))
|
17 |
+
print(prediction)
|
18 |
+
return jsonify(prediction)
|
19 |
+
|
20 |
+
|
21 |
+
if __name__ == '__main__':
|
22 |
+
modelfile = 'model.pickle'
|
23 |
+
model = p.load(open(modelfile, 'rb'))
|
24 |
+
app.run(debug=True, host='0.0.0.0')
|
vercel.json
CHANGED
@@ -2,7 +2,7 @@
|
|
2 |
"version": 2,
|
3 |
"builds": [
|
4 |
{
|
5 |
-
"src": "./
|
6 |
"use": "@vercel/python"
|
7 |
}
|
8 |
],
|
|
|
2 |
"version": 2,
|
3 |
"builds": [
|
4 |
{
|
5 |
+
"src": "./server.py",
|
6 |
"use": "@vercel/python"
|
7 |
}
|
8 |
],
|