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Add Jupyter notebook to keep track of experimentation. This is for pe…
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…rsonal documentation.
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@notadamking
notadamking committed Jul 8, 2019
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185 changes: 185 additions & 0 deletions .ipynb_checkpoints/Experiments-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0.00\n",
"1 0.40\n",
"2 1.00\n",
"3 0.20\n",
"4 0.44\n",
"5 0.58\n",
"Name: normalized_a, dtype: float64\n",
" a b c normalized_a\n",
"0 0.00 0.0000 0.000000 0.00\n",
"1 0.40 0.1875 0.416667 0.40\n",
"2 1.00 0.1500 0.166667 1.00\n",
"3 0.20 0.1375 1.000000 0.20\n",
"4 0.44 1.0000 0.033333 0.44\n",
"5 0.58 0.0500 0.000000 0.58\n",
"[[0. 0. 0. 0. ]\n",
" [0.4 0.1875 0.41666667 0.4 ]\n",
" [1. 0.15 0.16666667 1. ]\n",
" [0.2 0.1375 1. 0.2 ]\n",
" [0.44 1. 0.03333333 0.44 ]\n",
" [0.58 0.05 0. 0.58 ]]\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame([\n",
" { 'a': 0, 'b': 0, 'c': 0 },\n",
" { 'a': 2, 'b': 1.5, 'c': 2.5 },\n",
" { 'a': 5, 'b': 1.2, 'c': 1 },\n",
" { 'a': 1, 'b': 1.1, 'c': 6 },\n",
" { 'a': 2.2, 'b': 8, 'c': 0.2 },\n",
" { 'a': 2.9, 'b': 0.4, 'c': 0 },\n",
"])\n",
"\n",
"df['normalized_a'] = (df['a'] - df['a'].min()) / (df['a'].max() - df['a'].min())\n",
"\n",
"print(df['normalized_a'])\n",
"\n",
"from lib.data.features.transform import max_min_normalize\n",
"\n",
"df_max_min = max_min_normalize(df)\n",
"df_max_min_list = max_min_normalize(df.values)\n",
"\n",
"print(df_max_min)\n",
"print(df_max_min_list)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Date Open High Low Close Volume\n",
"1664 1417392000 300.0 370.0 300.00 370.0 0.05656\n",
"1663 1417478400 370.0 378.0 370.00 378.0 15.01000\n",
"1662 1417564800 378.0 378.0 377.01 378.0 0.54660\n",
"1661 1417651200 378.0 378.0 377.10 377.1 0.01000\n",
"1660 1417737600 377.1 377.1 377.10 377.1 0.00000\n",
" Date Open High Low Close Volume\n",
"1664 0.000061 0.209721 0.021391 0.209721 0.021391 5.581170\n",
"1663 0.000061 0.209721 0.021391 0.209721 0.021391 5.581170\n",
"1662 0.000061 0.021391 0.000000 0.018769 0.000000 -3.312755\n",
"1661 0.000061 0.000000 0.000000 0.000239 -0.002384 -4.001132\n",
"1660 0.000061 -0.002384 -0.002384 0.000000 0.000000 0.000000\n",
" Date Open High Low Close Volume\n",
"1664 0.008457 0.732499 0.455558 0.510581 0.592149 0.996942\n",
"1663 0.008457 0.732499 0.455558 0.510581 0.592149 0.996942\n",
"1662 0.008452 0.592149 0.415140 0.500916 0.576208 0.244405\n",
"1661 0.008446 0.576208 0.415140 0.499978 0.574431 0.186160\n",
"1660 0.008441 0.574431 0.410636 0.499966 0.576208 0.524705\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/Adam/Desktop/YouTube/BitcoinTrader/lib/data/features/transform.py:54: RuntimeWarning: divide by zero encountered in log\n",
" return transform(iterable, inplace, columns, lambda t_iterable: np.log(t_iterable) - np.log(t_iterable).shift(1))\n"
]
}
],
"source": [
"from lib.data.providers import ProviderDateFormat, StaticDataProvider\n",
"from lib.data.features.transform import max_min_normalize, log_and_difference\n",
"\n",
"data_columns = {'Date': 'Date', 'Open': 'Open', 'High': 'High',\n",
" 'Low': 'Low', 'Close': 'Close', 'Volume': 'VolumeFrom'}\n",
"\n",
"provider = StaticDataProvider(date_format=ProviderDateFormat.DATETIME_HOUR_24,\n",
" csv_data_path=\"/Users/Adam/Desktop/YouTube/BitcoinTrader/data/input/coinbase-1d-btc-usd.csv\",\n",
" data_columns=data_columns)\n",
"\n",
"print(provider.data_frame.head())\n",
"\n",
"logged_and_diffed = log_and_difference(provider.data_frame)\n",
"\n",
"print(logged_and_diffed.head())\n",
"\n",
"normalized = max_min_normalize(provider.data_frame)\n",
"\n",
"print(normalized.head())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-3.]\n",
" [-3.]\n",
" [ 5.]\n",
" [ 1.]\n",
" [ 1.]]\n",
"[[0. ]\n",
" [0. ]\n",
" [1. ]\n",
" [0.5]\n",
" [0.5]]\n",
"[0. 0. 1. 0.5 0.5]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"from lib.data.features.transform import log_and_difference, max_min_normalize, difference\n",
"\n",
"items = [1, -2, 3, 4, 5]\n",
"\n",
"diffed = difference(items, inplace=False)\n",
"\n",
"print(diffed)\n",
"\n",
"normed = max_min_normalize(logged)\n",
"\n",
"print(normed)\n",
"\n",
"raveled = np.ravel(normed, order='F')\n",
"\n",
"print(raveled)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
192 changes: 192 additions & 0 deletions Experiments.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0.00\n",
"1 0.40\n",
"2 1.00\n",
"3 0.20\n",
"4 0.44\n",
"5 0.58\n",
"Name: normalized_a, dtype: float64\n",
" a b c normalized_a\n",
"0 0.00 0.0000 0.000000 0.00\n",
"1 0.40 0.1875 0.416667 0.40\n",
"2 1.00 0.1500 0.166667 1.00\n",
"3 0.20 0.1375 1.000000 0.20\n",
"4 0.44 1.0000 0.033333 0.44\n",
"5 0.58 0.0500 0.000000 0.58\n",
"[[0. 0. 0. 0. ]\n",
" [0.4 0.1875 0.41666667 0.4 ]\n",
" [1. 0.15 0.16666667 1. ]\n",
" [0.2 0.1375 1. 0.2 ]\n",
" [0.44 1. 0.03333333 0.44 ]\n",
" [0.58 0.05 0. 0.58 ]]\n"
]
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.DataFrame([\n",
" { 'a': 0, 'b': 0, 'c': 0 },\n",
" { 'a': 2, 'b': 1.5, 'c': 2.5 },\n",
" { 'a': 5, 'b': 1.2, 'c': 1 },\n",
" { 'a': 1, 'b': 1.1, 'c': 6 },\n",
" { 'a': 2.2, 'b': 8, 'c': 0.2 },\n",
" { 'a': 2.9, 'b': 0.4, 'c': 0 },\n",
"])\n",
"\n",
"df['normalized_a'] = (df['a'] - df['a'].min()) / (df['a'].max() - df['a'].min())\n",
"\n",
"print(df['normalized_a'])\n",
"\n",
"from lib.data.features.transform import max_min_normalize\n",
"\n",
"df_max_min = max_min_normalize(df)\n",
"df_max_min_list = max_min_normalize(df.values)\n",
"\n",
"print(df_max_min)\n",
"print(df_max_min_list)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Date Open High Low Close Volume\n",
"1664 1417392000 300.0 370.0 300.00 370.0 0.05656\n",
"1663 1417478400 370.0 378.0 370.00 378.0 15.01000\n",
"1662 1417564800 378.0 378.0 377.01 378.0 0.54660\n",
"1661 1417651200 378.0 378.0 377.10 377.1 0.01000\n",
"1660 1417737600 377.1 377.1 377.10 377.1 0.00000\n",
" Date Open High Low Close Volume\n",
"1664 0.000061 0.209721 0.021391 0.209721 0.021391 5.581170\n",
"1663 0.000061 0.209721 0.021391 0.209721 0.021391 5.581170\n",
"1662 0.000061 0.021391 0.000000 0.018769 0.000000 -3.312755\n",
"1661 0.000061 0.000000 0.000000 0.000239 -0.002384 -4.001132\n",
"1660 0.000061 -0.002384 -0.002384 0.000000 0.000000 0.000000\n",
" Date Open High Low Close Volume\n",
"1664 0.008457 0.732499 0.455558 0.510581 0.592149 0.996942\n",
"1663 0.008457 0.732499 0.455558 0.510581 0.592149 0.996942\n",
"1662 0.008452 0.592149 0.415140 0.500916 0.576208 0.244405\n",
"1661 0.008446 0.576208 0.415140 0.499978 0.574431 0.186160\n",
"1660 0.008441 0.574431 0.410636 0.499966 0.576208 0.524705\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/Adam/Desktop/YouTube/BitcoinTrader/lib/data/features/transform.py:54: RuntimeWarning: divide by zero encountered in log\n",
" return transform(iterable, inplace, columns, lambda t_iterable: np.log(t_iterable) - np.log(t_iterable).shift(1))\n"
]
}
],
"source": [
"from lib.data.providers import ProviderDateFormat, StaticDataProvider\n",
"from lib.data.features.transform import max_min_normalize, log_and_difference\n",
"\n",
"data_columns = {'Date': 'Date', 'Open': 'Open', 'High': 'High',\n",
" 'Low': 'Low', 'Close': 'Close', 'Volume': 'VolumeFrom'}\n",
"\n",
"provider = StaticDataProvider(date_format=ProviderDateFormat.DATETIME_HOUR_24,\n",
" csv_data_path=\"/Users/Adam/Desktop/YouTube/BitcoinTrader/data/input/coinbase-1d-btc-usd.csv\",\n",
" data_columns=data_columns)\n",
"\n",
"print(provider.data_frame.head())\n",
"\n",
"logged_and_diffed = log_and_difference(provider.data_frame)\n",
"\n",
"print(logged_and_diffed.head())\n",
"\n",
"normalized = max_min_normalize(provider.data_frame)\n",
"\n",
"print(normalized.head())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-3.]\n",
" [-3.]\n",
" [ 5.]\n",
" [ 1.]\n",
" [ 1.]]\n",
"[[0. ]\n",
" [0. ]\n",
" [1. ]\n",
" [0.5]\n",
" [0.5]]\n",
"[0. 0. 1. 0.5 0.5]\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"from lib.data.features.transform import log_and_difference, max_min_normalize, difference\n",
"\n",
"items = [1, -2, 3, 4, 5]\n",
"\n",
"diffed = difference(items, inplace=False)\n",
"\n",
"print(diffed)\n",
"\n",
"normed = max_min_normalize(logged)\n",
"\n",
"print(normed)\n",
"\n",
"raveled = np.ravel(normed, order='F')\n",
"\n",
"print(raveled)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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