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Added the points per score at turn label.

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Philipp committed 2023-02-18 00:12:29 +01:00
1 parent e199c9ab55
commit c0943e4309
3 files changed
+178 -62
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main.ipynb
+109 -37
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@@ -1373,13 +1373,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 119, "execution_count": 145,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"application/vnd.jupyter.widget-view+json": { "application/vnd.jupyter.widget-view+json": {
"model_id": "d4dc3ee2dff24deaaacebbf4e7e9dddf", "model_id": "b098bb4da154488b8b4c22722833e8c0",
"version_major": 2, "version_major": 2,
"version_minor": 0 "version_minor": 0
}, },
@@ -1414,7 +1414,7 @@
" range(70),\n", " range(70),\n",
" mean_possibilitie_count,\n", " mean_possibilitie_count,\n",
" yerr=std_possibilitie_count,\n", " yerr=std_possibilitie_count,\n",
" label='=\"Mean action space size with error bars',\n", " label=\"Mean action space size with error bars\",\n",
" )\n", " )\n",
" ax2.scatter(turn, mean_possibilitie_count[turn], marker=\"x\")\n", " ax2.scatter(turn, mean_possibilitie_count[turn], marker=\"x\")\n",
" ax2.legend()\n", " ax2.legend()\n",
@@ -1587,18 +1587,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 130, "execution_count": 146,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"application/vnd.jupyter.widget-view+json": { "application/vnd.jupyter.widget-view+json": {
"model_id": "679fea405f704503ae407321cab3779a", "model_id": "75ffc8765b074cd0b4495ac6075bb6b8",
"version_major": 2, "version_major": 2,
"version_minor": 0 "version_minor": 0
}, },
"text/plain": [ "text/plain": [
"interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…" "interactive(children=(IntSlider(value=29, description='turn', max=59), Output()), _dom_classes=('widget-intera…"
] ]
}, },
"metadata": {}, "metadata": {},
@@ -1610,7 +1610,7 @@
"score_history[1::2] = score_history[1::2] * -1\n", "score_history[1::2] = score_history[1::2] * -1\n",
"\n", "\n",
"\n", "\n",
"@interact(turn=(0, 69))\n", "@interact(turn=(0, 59))\n",
"def hist_direct_score(turn):\n", "def hist_direct_score(turn):\n",
" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 7))\n", " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 7))\n",
" fig.suptitle(\n", " fig.suptitle(\n",
@@ -1618,39 +1618,43 @@
" )\n", " )\n",
"\n", "\n",
" ax1.set_title(\n", " ax1.set_title(\n",
" f\"Histogram of turn {turn} by {'white' if turn % 2 == 0 else 'black'}\"\n", " f\"Histogram of scores on turn {turn} by {'white' if turn % 2 == 0 else 'black'}\"\n",
" )\n", " )\n",
"\n", "\n",
" ax1.hist(score_history[turn], density=True)\n", " ax1.hist(score_history[turn], density=True)\n",
" ax1.set_xlabel(\"Action space size\")\n", " ax1.set_xlabel(\"Points made\")\n",
" ax1.set_ylabel(\"Action space size probability\")\n", " ax1.set_ylabel(\"Score probability\")\n",
" ax2.set_title(f\"Mean size of the action space per turn\")\n", " ax2.set_title(f\"Points scored at turn\")\n",
" ax2.set_xlabel(\"Turn\")\n", " ax2.set_xlabel(\"Turn\")\n",
" ax2.set_ylabel(\"Average possible moves\")\n", " ax2.set_ylabel(\"Average points scored\")\n",
"\n", "\n",
" ax2.errorbar(\n", " ax2.errorbar(\n",
" range(70),\n", " range(60),\n",
" mean_possibilitie_count,\n", " np.mean(score_history, axis=1)[:60],\n",
" yerr=std_possibilitie_count,\n", " yerr=np.std(score_history, axis=1)[:60],\n",
" label='=\"Mean action space size with error bars',\n", " label=\"Mean socre at turn\",\n",
" )\n", " )\n",
" ax2.scatter(turn, mean_possibilitie_count[turn], marker=\"x\")\n", " ax2.scatter(turn, np.mean(score_history, axis=1)[turn], marker=\"x\", color=\"red\")\n",
" ax2.legend()\n", " ax2.legend()\n",
" plt.show()" " plt.show()"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 147,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
}, "data": {
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truncated
"cell_type": "code", "text/plain": [
"execution_count": null, "<Figure size 640x480 with 1 Axes>"
"metadata": {}, ]
"outputs": [], },
"metadata": {},
"output_type": "display_data"
}
],
"source": [ "source": [
"def calculate_final_evaluation_for_history(board_history: np.ndarray) -> np.ndarray:\n", "def calculate_final_evaluation_for_history(board_history: np.ndarray) -> np.ndarray:\n",
" final_evaluation = final_boards_evaluation(board_history[-1])\n", " final_evaluation = final_boards_evaluation(board_history[-1])\n",
@@ -1658,7 +1662,6 @@
"\n", "\n",
"\n", "\n",
"assert len(calculate_final_evaluation_for_history(_board_history).shape) == 1\n", "assert len(calculate_final_evaluation_for_history(_board_history).shape) == 1\n",
"print(calculate_final_evaluation_for_history(_board_history).shape)\n",
"_final_eval = calculate_final_evaluation_for_history(_board_history)\n", "_final_eval = calculate_final_evaluation_for_history(_board_history)\n",
"plt.title(\"Histogram over the score distribtuion\")\n", "plt.title(\"Histogram over the score distribtuion\")\n",
"plt.hist((_final_eval * 64), density=True)\n", "plt.hist((_final_eval * 64), density=True)\n",
@@ -1667,9 +1670,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 148,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [ "source": [
"def calculate_who_won(board_history: np.ndarray) -> np.ndarray:\n", "def calculate_who_won(board_history: np.ndarray) -> np.ndarray:\n",
" who_won = evaluate_who_won(board_history[-1])\n", " who_won = evaluate_who_won(board_history[-1])\n",
@@ -1683,11 +1697,22 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 149,
"metadata": { "metadata": {
"scrolled": false "scrolled": false
}, },
"outputs": [], "outputs": [
{
"data": {
"image/png": "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 truncated
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [ "source": [
"def history_changed(board_history: np.ndarray) -> np.ndarray:\n", "def history_changed(board_history: np.ndarray) -> np.ndarray:\n",
" return ~np.all(\n", " return ~np.all(\n",
@@ -1703,9 +1728,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 150,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"(70, 10000)"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"def get_gamma_table(board_history, gamma_value: float):\n", "def get_gamma_table(board_history, gamma_value: float):\n",
" unchanged = history_changed(board_history)\n", " unchanged = history_changed(board_history)\n",
@@ -1719,9 +1755,33 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 151,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"array([0.06513542, 0.02282552, 0.08712565, 0.05031332, 0.1214854 ,\n",
" 0.06314092, 0.14037841, 0.0759323 , 0.16290323, 0.08841437,\n",
" 0.18861413, 0.10899313, 0.19318856, 0.10964757, 0.20731361,\n",
" 0.13148691, 0.22510605, 0.13292382, 0.23539045, 0.12585808,\n",
" 0.23848829, 0.15176572, 0.26641954, 0.17761089, 0.28060736,\n",
" 0.16112694, 0.31286326, 0.17623532, 0.28714693, 0.16549503,\n",
" 0.33439531, 0.19199073, 0.29858216, 0.20875418, 0.33700629,\n",
" 0.1993395 , 0.36539974, 0.24731134, 0.36773293, 0.24404735,\n",
" 0.42837075, 0.28564118, 0.41564522, 0.28406984, 0.41368105,\n",
" 0.2341238 , 0.39596409, 0.26595149, 0.39103311, 0.38067557,\n",
" 0.47846166, 0.30745852, 0.4819794 , 0.38419044, 0.5611122 ,\n",
" 0.524575 , 0.546425 , 0.466925 , 0.601625 , 0.570625 ,\n",
" 0.570625 , 0.35625 , 0.36875 , 0.36875 , 0.38125 ,\n",
" 0.38125 , 0.38125 , 0.38125 , 0.38125 , 0.39715854])"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"def calculate_q_reword(\n", "def calculate_q_reword(\n",
" board_history: np.ndarray,\n", " board_history: np.ndarray,\n",
@@ -1754,9 +1814,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 152,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "NameError",
"evalue": "name 'rewords' is not defined",
"output_type": "error",
"traceback": [
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[1;32mIn[152], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[43mrewords\u001B[49m\n\u001B[0;32m 2\u001B[0m evaluate_boards(boards)\u001B[38;5;241m.\u001B[39mshape\n",
"\u001B[1;31mNameError\u001B[0m: name 'rewords' is not defined"
]
}
],
"source": [ "source": [
"rewords\n", "rewords\n",
"evaluate_boards(boards).shape" "evaluate_boards(boards).shape"
poetry.lock
Generated
+68 -25
View File
@@ -496,26 +496,6 @@ pyqt5 = ["pyqt5"]
pyside6 = ["pyside6"] pyside6 = ["pyside6"]
test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"] test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio", "pytest-cov", "pytest-timeout"]
[[package]]
name = "ipympl"
version = "0.9.3"
description = "Matplotlib Jupyter Extension"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
ipython = "<9"
ipython-genutils = "*"
ipywidgets = ">=7.6.0,<9"
matplotlib = ">=3.4.0,<4"
numpy = "*"
pillow = "*"
traitlets = "<6"
[package.extras]
docs = ["Sphinx (>=1.5)", "myst-nb", "sphinx-book-theme", "sphinx-copybutton", "sphinx-thebe", "sphinx-togglebutton"]
[[package]] [[package]]
name = "ipython" name = "ipython"
version = "8.10.0" version = "8.10.0"
@@ -1216,6 +1196,22 @@ category = "main"
optional = false optional = false
python-versions = ">=3.7" python-versions = ">=3.7"
[[package]]
name = "pandas"
version = "1.5.3"
description = "Powerful data structures for data analysis, time series, and statistics"
category = "main"
optional = false
python-versions = ">=3.8"
[package.dependencies]
numpy = {version = ">=1.21.0", markers = "python_version >= \"3.10\""}
python-dateutil = ">=2.8.1"
pytz = ">=2020.1"
[package.extras]
test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
[[package]] [[package]]
name = "pandocfilters" name = "pandocfilters"
version = "1.5.0" version = "1.5.0"
@@ -1586,6 +1582,24 @@ dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool",
doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"] doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
[[package]]
name = "seaborn"
version = "0.12.2"
description = "Statistical data visualization"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
matplotlib = ">=3.1,<3.6.1 || >3.6.1"
numpy = ">=1.17,<1.24.0 || >1.24.0"
pandas = ">=0.25"
[package.extras]
dev = ["flake8", "flit", "mypy", "pandas-stubs", "pre-commit", "pytest", "pytest-cov", "pytest-xdist"]
docs = ["ipykernel", "nbconvert", "numpydoc", "pydata_sphinx_theme (==0.10.0rc2)", "pyyaml", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
stats = ["scipy (>=1.3)", "statsmodels (>=0.10)"]
[[package]] [[package]]
name = "send2trash" name = "send2trash"
version = "1.8.0" version = "1.8.0"
@@ -1937,7 +1951,7 @@ test = ["mypy", "pre-commit", "pytest", "pytest-asyncio", "websockets (>=10.0)"]
[metadata] [metadata]
lock-version = "1.1" lock-version = "1.1"
python-versions = "3.10.*" python-versions = "3.10.*"
content-hash = "70ad716cf2af3d060355d2f419fa295002e6fa9d474842b892e0e886d9d9a3d9" content-hash = "14a2ea88e851f7293e4c3b8f7b49cedf58dabc3340cba1644bd52db682d8d7d8"
[metadata.files] [metadata.files]
aiofiles = [ aiofiles = [
@@ -2338,10 +2352,6 @@ ipykernel = [
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{file = "ipykernel-6.21.2.tar.gz", hash = "sha256:6e9213484e4ce1fb14267ee435e18f23cc3a0634e635b9fb4ed4677b84e0fdf8"}, {file = "ipykernel-6.21.2.tar.gz", hash = "sha256:6e9213484e4ce1fb14267ee435e18f23cc3a0634e635b9fb4ed4677b84e0fdf8"},
] ]
ipympl = [
{file = "ipympl-0.9.3-py2.py3-none-any.whl", hash = "sha256:d113cd55891bafe9b27ef99b6dd111a87beb6bb2ae550c404292272103be8013"},
{file = "ipympl-0.9.3.tar.gz", hash = "sha256:49bab75c05673a6881d1aaec5d8ac81d4624f73d292d154c5fb7096f10236a2b"},
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ipython = [ ipython = [
{file = "ipython-8.10.0-py3-none-any.whl", hash = "sha256:b38c31e8fc7eff642fc7c597061fff462537cf2314e3225a19c906b7b0d8a345"}, {file = "ipython-8.10.0-py3-none-any.whl", hash = "sha256:b38c31e8fc7eff642fc7c597061fff462537cf2314e3225a19c906b7b0d8a345"},
{file = "ipython-8.10.0.tar.gz", hash = "sha256:b13a1d6c1f5818bd388db53b7107d17454129a70de2b87481d555daede5eb49e"}, {file = "ipython-8.10.0.tar.gz", hash = "sha256:b13a1d6c1f5818bd388db53b7107d17454129a70de2b87481d555daede5eb49e"},
@@ -2763,6 +2773,35 @@ packaging = [
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{file = "packaging-23.0.tar.gz", hash = "sha256:b6ad297f8907de0fa2fe1ccbd26fdaf387f5f47c7275fedf8cce89f99446cf97"}, {file = "packaging-23.0.tar.gz", hash = "sha256:b6ad297f8907de0fa2fe1ccbd26fdaf387f5f47c7275fedf8cce89f99446cf97"},
] ]
pandas = [
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pandocfilters = [ pandocfilters = [
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{file = "pandocfilters-1.5.0.tar.gz", hash = "sha256:0b679503337d233b4339a817bfc8c50064e2eff681314376a47cb582305a7a38"}, {file = "pandocfilters-1.5.0.tar.gz", hash = "sha256:0b679503337d233b4339a817bfc8c50064e2eff681314376a47cb582305a7a38"},
@@ -3155,6 +3194,10 @@ scipy = [
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] ]
seaborn = [
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send2trash = [ send2trash = [
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pyproject.toml
+1
View File
@@ -23,6 +23,7 @@ torchaudio = "^0.13.1"
gym = "^0.26.2" gym = "^0.26.2"
kdepy = "^1.1.0" kdepy = "^1.1.0"
plotly = "^5.13.0" plotly = "^5.13.0"
seaborn = "^0.12.2"
[tool.poetry.group.build.dependencies] [tool.poetry.group.build.dependencies]
blackcellmagic = "^0.0.3" blackcellmagic = "^0.0.3"