Skip to content

Commit e1d4e4b

Browse files
Apoorvgarg-creatorApoorvgarg-creator
committed
reinforcement learning, Incremental learning and data added
1 parent 80dded1 commit e1d4e4b

File tree

8 files changed

+744
-0
lines changed

8 files changed

+744
-0
lines changed

‎Reinforcement Learning/Untitled.ipynb

+485
Large diffs are not rendered by default.

‎Reinforcement Learning/cartpole_model/weights_0000.hdf5

17.8 KB
Binary file not shown.

‎Reinforcement Learning/cartpole_model/weights_0050.hdf5

17.8 KB
Binary file not shown.

‎Reinforcement Learning/cartpole_model/weights_0100.hdf5

17.8 KB
Binary file not shown.

‎creme_tut/Creme_TUT.ipynb

+36
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,36 @@
1+
{
2+
"cells": [
3+
{
4+
"cell_type": "code",
5+
"execution_count": null,
6+
"metadata": {
7+
"collapsed": true
8+
},
9+
"outputs": [],
10+
"source": [
11+
"\n"
12+
]
13+
}
14+
],
15+
"metadata": {
16+
"kernelspec": {
17+
"display_name": "Python 3",
18+
"language": "python",
19+
"name": "python3"
20+
},
21+
"language_info": {
22+
"codemirror_mode": {
23+
"name": "ipython",
24+
"version": 2
25+
},
26+
"file_extension": ".py",
27+
"mimetype": "text/x-python",
28+
"name": "python",
29+
"nbconvert_exporter": "python",
30+
"pygments_lexer": "ipython2",
31+
"version": "2.7.6"
32+
}
33+
},
34+
"nbformat": 4,
35+
"nbformat_minor": 0
36+
}

‎creme_tut/Creme_tut_1.ipynb

+223
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,223 @@
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+
"Requirement already satisfied: creme in /Users/apoorvgarg/opt/anaconda3/lib/python3.8/site-packages (0.6.1)\n",
13+
"Requirement already satisfied: numpy>=1.18.1 in /Users/apoorvgarg/opt/anaconda3/lib/python3.8/site-packages (from creme) (1.18.5)\n",
14+
"Requirement already satisfied: scipy>=1.4.1 in /Users/apoorvgarg/opt/anaconda3/lib/python3.8/site-packages (from creme) (1.4.1)\n",
15+
"Requirement already satisfied: pandas>=1.0.1 in /Users/apoorvgarg/opt/anaconda3/lib/python3.8/site-packages (from creme) (1.0.5)\n",
16+
"Requirement already satisfied: mmh3==2.5.1 in /Users/apoorvgarg/opt/anaconda3/lib/python3.8/site-packages (from creme) (2.5.1)\n",
17+
"Requirement already satisfied: python-dateutil>=2.6.1 in /Users/apoorvgarg/opt/anaconda3/lib/python3.8/site-packages (from pandas>=1.0.1->creme) (2.8.1)\n",
18+
"Requirement already satisfied: pytz>=2017.2 in /Users/apoorvgarg/opt/anaconda3/lib/python3.8/site-packages (from pandas>=1.0.1->creme) (2020.1)\n",
19+
"Requirement already satisfied: six>=1.5 in /Users/apoorvgarg/opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.6.1->pandas>=1.0.1->creme) (1.15.0)\n"
20+
]
21+
}
22+
],
23+
"source": [
24+
"!pip install creme"
25+
]
26+
},
27+
{
28+
"cell_type": "code",
29+
"execution_count": 5,
30+
"metadata": {},
31+
"outputs": [],
32+
"source": [
33+
"import creme, math\n",
34+
"from creme import compose\n",
35+
"from creme import feature_extraction\n",
36+
"from creme import naive_bayes"
37+
]
38+
},
39+
{
40+
"cell_type": "code",
41+
"execution_count": 3,
42+
"metadata": {},
43+
"outputs": [],
44+
"source": [
45+
"# List of tuple \n",
46+
"# creme accepts input as list of tuple\n",
47+
"docs = [ ('Chinese beijing Chinese','yes'),\n",
48+
" ('Chinese Chinese Shanghai','yes'),\n",
49+
" ('Chinese Macao','yes'),\n",
50+
" ('Tokyo Japan Chinese','no')\n",
51+
" ]"
52+
]
53+
},
54+
{
55+
"cell_type": "code",
56+
"execution_count": 4,
57+
"metadata": {},
58+
"outputs": [
59+
{
60+
"data": {
61+
"text/plain": [
62+
"[('Chinese beijing Chinese', 'yes'),\n",
63+
" ('Chinese Chinese Shanghai', 'yes'),\n",
64+
" ('Chinese Macao', 'yes'),\n",
65+
" ('Tokyo Japan Chinese', 'no')]"
66+
]
67+
},
68+
"execution_count": 4,
69+
"metadata": {},
70+
"output_type": "execute_result"
71+
}
72+
],
73+
"source": [
74+
"docs "
75+
]
76+
},
77+
{
78+
"cell_type": "code",
79+
"execution_count": 6,
80+
"metadata": {},
81+
"outputs": [],
82+
"source": [
83+
"model = compose.Pipeline(\n",
84+
" ('tokenize', feature_extraction.BagOfWords(lowercase=False)),\n",
85+
" ('nb',naive_bayes.MultinomialNB(alpha=1))\n",
86+
")"
87+
]
88+
},
89+
{
90+
"cell_type": "code",
91+
"execution_count": 7,
92+
"metadata": {},
93+
"outputs": [],
94+
"source": [
95+
"for sentence, label in docs:\n",
96+
" model = model.fit_one(sentence,label)"
97+
]
98+
},
99+
{
100+
"cell_type": "code",
101+
"execution_count": 8,
102+
"metadata": {},
103+
"outputs": [],
104+
"source": [
105+
"new_unseen_text = 'Tokyo india'"
106+
]
107+
},
108+
{
109+
"cell_type": "code",
110+
"execution_count": 9,
111+
"metadata": {},
112+
"outputs": [
113+
{
114+
"data": {
115+
"text/plain": [
116+
"'no'"
117+
]
118+
},
119+
"execution_count": 9,
120+
"metadata": {},
121+
"output_type": "execute_result"
122+
}
123+
],
124+
"source": [
125+
"model.predict_one(new_unseen_text)"
126+
]
127+
},
128+
{
129+
"cell_type": "code",
130+
"execution_count": null,
131+
"metadata": {},
132+
"outputs": [],
133+
"source": []
134+
},
135+
{
136+
"cell_type": "markdown",
137+
"metadata": {},
138+
"source": [
139+
"### Training on a new Data and new category"
140+
]
141+
},
142+
{
143+
"cell_type": "code",
144+
"execution_count": 17,
145+
"metadata": {},
146+
"outputs": [
147+
{
148+
"data": {
149+
"text/plain": [
150+
"Pipeline (\n",
151+
" BagOfWords (\n",
152+
" on=None\n",
153+
" strip_accents=True\n",
154+
" lowercase=False\n",
155+
" preprocessor=None\n",
156+
" tokenizer=<built-in method findall of re.Pattern object at 0x7fc7f4fef6b0>\n",
157+
" ngram_range=(1, 1)\n",
158+
" ),\n",
159+
" MultinomialNB (\n",
160+
" alpha=1\n",
161+
" )\n",
162+
")"
163+
]
164+
},
165+
"execution_count": 17,
166+
"metadata": {},
167+
"output_type": "execute_result"
168+
}
169+
],
170+
"source": [
171+
"model.fit_one('France Africa','may be')"
172+
]
173+
},
174+
{
175+
"cell_type": "code",
176+
"execution_count": 19,
177+
"metadata": {},
178+
"outputs": [
179+
{
180+
"data": {
181+
"text/plain": [
182+
"'may be'"
183+
]
184+
},
185+
"execution_count": 19,
186+
"metadata": {},
187+
"output_type": "execute_result"
188+
}
189+
],
190+
"source": [
191+
"model.predict_one(\"Africa Delhi\")"
192+
]
193+
},
194+
{
195+
"cell_type": "code",
196+
"execution_count": null,
197+
"metadata": {},
198+
"outputs": [],
199+
"source": []
200+
}
201+
],
202+
"metadata": {
203+
"kernelspec": {
204+
"display_name": "Python 3",
205+
"language": "python",
206+
"name": "python3"
207+
},
208+
"language_info": {
209+
"codemirror_mode": {
210+
"name": "ipython",
211+
"version": 3
212+
},
213+
"file_extension": ".py",
214+
"mimetype": "text/x-python",
215+
"name": "python",
216+
"nbconvert_exporter": "python",
217+
"pygments_lexer": "ipython3",
218+
"version": "3.8.3"
219+
}
220+
},
221+
"nbformat": 4,
222+
"nbformat_minor": 4
223+
}

‎data/ Apoorv.npy

29.4 KB
Binary file not shown.

‎data/ Appy.npy

88 KB
Binary file not shown.

0 commit comments

Comments
 (0)