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BenSchZA/notebook.ipynb

Created September 4, 2020 13:53
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notebook.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"import cadCAD\n",
"cadCAD.__file__"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from cadCAD.configuration.utils import config_sim\n",
"from cadCAD.configuration import Experiment\n",
"from cadCAD.engine import ExecutionMode, ExecutionContext\n",
"from cadCAD.engine import Executor\n",
"\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"system_params = {\n",
" 'add': [10]\n",
"}\n",
"\n",
"initial_state = {\n",
" 'a': 0\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from numpy import random\n",
"\n",
"def update_a(params, substep, state_history, previous_state, policy_input, **kwargs):\n",
" return 'a', previous_state['a'] + params['add'] * random.rand()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"psubs = [\n",
" {\n",
" 'policies': {},\n",
" 'variables': {\n",
" 'a': update_a\n",
" }\n",
" }\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from cadCAD import configs\n",
"del configs[:] # Clear any prior configs"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 0, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 0, 'run_id': 0}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 0, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 0, 'run_id': 1}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 0, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 0, 'run_id': 2}]\n"
]
}
],
"source": [
"exp = Experiment()\n",
"\n",
"sim_config = config_sim({\n",
" \"N\": 3,\n",
" \"T\": range(100),\n",
" \"M\": system_params\n",
"})\n",
"# print(sim_config)\n",
"\n",
"exp.append_configs(\n",
" initial_state = initial_state,\n",
" partial_state_update_blocks = psubs,\n",
" sim_configs = sim_config\n",
")\n",
"# print(sim_config)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" ___________ ____\n",
" ________ __ ___/ / ____/ | / __ \\\n",
" / ___/ __` / __ / / / /| | / / / /\n",
"/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
"\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\n",
"by cadCAD\n",
"\n",
"Execution Mode: local_proc\n",
"Configuration Count: 1\n",
"Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (100, 1, 3, 1)\n",
"Execution Method: local_simulations\n",
"SimIDs : [0, 0, 0]\n",
"SubsetIDs: [0, 0, 0]\n",
"Ns : [0, 1, 2]\n",
"ExpIDs : [0, 0, 0]\n",
"Execution Mode: parallelized\n",
"Total execution time: 0.03s\n"
]
},
{
"data": {
"text/plain": [
"'multi_proc'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exec_mode = ExecutionMode()\n",
"local_mode_ctx = ExecutionContext(context=exec_mode.local_mode)\n",
"\n",
"simulation = Executor(exec_context=local_mode_ctx, configs=configs)\n",
"\n",
"raw_system_events, tensor_field, sessions = simulation.execute()\n",
"\n",
"exec_mode.multi_proc"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=0_0',\n",
" 'simulation_id': 0,\n",
" 'run_id': 0,\n",
" 'subset_id': 0,\n",
" 'subset_window': deque([0, 0])},\n",
" {'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=0_1',\n",
" 'simulation_id': 0,\n",
" 'run_id': 1,\n",
" 'subset_id': 0,\n",
" 'subset_window': deque([0, 0])},\n",
" {'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=0_2',\n",
" 'simulation_id': 0,\n",
" 'run_id': 2,\n",
" 'subset_id': 0,\n",
" 'subset_window': deque([0, 0])}]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sessions"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" <td>13.807088</td>\n",
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" <th>4</th>\n",
" <td>19.579460</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
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" <th>...</th>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>298</th>\n",
" <td>462.187262</td>\n",
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" <td>96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>299</th>\n",
" <td>464.684905</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>300</th>\n",
" <td>467.214356</td>\n",
" <td>0</td>\n",
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" <td>474.217380</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>303 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" a simulation subset run substep timestep\n",
"0 0.000000 0 0 1 0 0\n",
"1 7.201762 0 0 1 1 1\n",
"2 12.985155 0 0 1 1 2\n",
"3 13.807088 0 0 1 1 3\n",
"4 19.579460 0 0 1 1 4\n",
".. ... ... ... ... ... ...\n",
"298 462.187262 0 0 3 1 96\n",
"299 464.684905 0 0 3 1 97\n",
"300 467.214356 0 0 3 1 98\n",
"301 473.321282 0 0 3 1 99\n",
"302 474.217380 0 0 3 1 100\n",
"\n",
"[303 rows x 6 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"simulation_result = pd.DataFrame(raw_system_events)\n",
"simulation_result"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"df = simulation_result.copy()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <td>7.201762</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" a simulation subset run substep timestep\n",
"0 0.000000 0 0 1 0 0\n",
"1 7.201762 0 0 1 1 1\n",
"2 12.985155 0 0 1 1 2\n",
"3 13.807088 0 0 1 1 3\n",
"4 19.579460 0 0 1 1 4"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['run'] == 1].head()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr>\n",
" <th>102</th>\n",
" <td>1.139284</td>\n",
" <td>0</td>\n",
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" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>4.291707</td>\n",
" <td>0</td>\n",
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" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>5.452919</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>11.419502</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a simulation subset run substep timestep\n",
"101 0.000000 0 0 2 0 0\n",
"102 1.139284 0 0 2 1 1\n",
"103 4.291707 0 0 2 1 2\n",
"104 5.452919 0 0 2 1 3\n",
"105 11.419502 0 0 2 1 4"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['run'] == 2].head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>run</th>\n",
" <th>substep</th>\n",
" <th>timestep</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>202</th>\n",
" <td>0.000000</td>\n",
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" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>6.796407</td>\n",
" <td>0</td>\n",
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" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>204</th>\n",
" <td>12.630302</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>205</th>\n",
" <td>13.932469</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>206</th>\n",
" <td>20.626006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" a simulation subset run substep timestep\n",
"202 0.000000 0 0 3 0 0\n",
"203 6.796407 0 0 3 1 1\n",
"204 12.630302 0 0 3 1 2\n",
"205 13.932469 0 0 3 1 3\n",
"206 20.626006 0 0 3 1 4"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['run'] == 3].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"system_params = {\n",
" 'add': [10,10,10]\n",
"}\n",
"\n",
"initial_state = {\n",
" 'a': 0\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"from numpy import random\n",
"\n",
"def update_a(params, substep, state_history, previous_state, policy_input, **kwargs):\n",
" return 'a', previous_state['a'] + params['add'] * random.rand()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"psubs = [\n",
" {\n",
" 'policies': {},\n",
" 'variables': {\n",
" 'a': update_a\n",
" }\n",
" }\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from cadCAD import configs\n",
"del configs[:] # Clear any prior configs"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 0, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 0, 'run_id': 0}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 0, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 0, 'run_id': 1}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 0, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 0, 'run_id': 2}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 1, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 1, 'run_id': 0}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 1, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 1, 'run_id': 1}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 1, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 1, 'run_id': 2}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 2, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 2, 'run_id': 0}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 2, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 2, 'run_id': 1}, {'N': 1, 'T': range(0, 100), 'M': {'add': 10}, 'subset_id': 2, 'subset_window': deque([0, None], maxlen=2), 'simulation_id': 2, 'run_id': 2}]\n"
]
}
],
"source": [
"exp = Experiment()\n",
"\n",
"sim_config = config_sim({\n",
" \"N\": 3,\n",
" \"T\": range(100),\n",
" \"M\": system_params\n",
"})\n",
"\n",
"exp.append_configs(\n",
" initial_state = initial_state,\n",
" partial_state_update_blocks = psubs,\n",
" sim_configs = sim_config\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" ___________ ____\n",
" ________ __ ___/ / ____/ | / __ \\\n",
" / ___/ __` / __ / / / /| | / / / /\n",
"/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n",
"\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\n",
"by cadCAD\n",
"\n",
"Execution Mode: local_proc\n",
"Configuration Count: 3\n",
"Dimensions of the first simulation: (Timesteps, Params, Runs, Vars) = (100, 1, 3, 1)\n",
"Execution Method: local_simulations\n",
"SimIDs : [0, 0, 0, 1, 1, 1, 2, 2, 2]\n",
"SubsetIDs: [0, 0, 0, 1, 1, 1, 2, 2, 2]\n",
"Ns : [0, 1, 2, 0, 1, 2, 0, 1, 2]\n",
"ExpIDs : [0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"Execution Mode: parallelized\n",
"Total execution time: 0.06s\n"
]
}
],
"source": [
"exec_mode = ExecutionMode()\n",
"local_mode_ctx = ExecutionContext(context=exec_mode.local_mode)\n",
"\n",
"simulation = Executor(exec_context=local_mode_ctx, configs=configs)\n",
"\n",
"raw_system_events, tensor_field, sessions = simulation.execute()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=0_0',\n",
" 'simulation_id': 0,\n",
" 'run_id': 0,\n",
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" 'subset_window': deque([2, 2])},\n",
" {'user_id': 'cadCAD_user',\n",
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" 'session_id': 'cadCAD_user=0_1',\n",
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" 'subset_id': 0,\n",
" 'subset_window': deque([2, 2])},\n",
" {'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=0_2',\n",
" 'simulation_id': 0,\n",
" 'run_id': 2,\n",
" 'subset_id': 0,\n",
" 'subset_window': deque([2, 2])},\n",
" {'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=1_0',\n",
" 'simulation_id': 1,\n",
" 'run_id': 0,\n",
" 'subset_id': 1,\n",
" 'subset_window': deque([2, 2])},\n",
" {'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=1_1',\n",
" 'simulation_id': 1,\n",
" 'run_id': 1,\n",
" 'subset_id': 1,\n",
" 'subset_window': deque([2, 2])},\n",
" {'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=1_2',\n",
" 'simulation_id': 1,\n",
" 'run_id': 2,\n",
" 'subset_id': 1,\n",
" 'subset_window': deque([2, 2])},\n",
" {'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=2_0',\n",
" 'simulation_id': 2,\n",
" 'run_id': 0,\n",
" 'subset_id': 2,\n",
" 'subset_window': deque([2, 2])},\n",
" {'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=2_1',\n",
" 'simulation_id': 2,\n",
" 'run_id': 1,\n",
" 'subset_id': 2,\n",
" 'subset_window': deque([2, 2])},\n",
" {'user_id': 'cadCAD_user',\n",
" 'experiment_id': 0,\n",
" 'session_id': 'cadCAD_user=2_2',\n",
" 'simulation_id': 2,\n",
" 'run_id': 2,\n",
" 'subset_id': 2,\n",
" 'subset_window': deque([2, 2])}]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sessions"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>909 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" a simulation subset run substep timestep\n",
"0 0.000000 0 0 1 0 0\n",
"1 3.584995 0 0 1 1 1\n",
"2 8.837808 0 0 1 1 2\n",
"3 11.780959 0 0 1 1 3\n",
"4 21.201066 0 0 1 1 4\n",
".. ... ... ... ... ... ...\n",
"904 484.280146 2 2 3 1 96\n",
"905 491.913474 2 2 3 1 97\n",
"906 497.662872 2 2 3 1 98\n",
"907 502.692637 2 2 3 1 99\n",
"908 512.387846 2 2 3 1 100\n",
"\n",
"[909 rows x 6 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"simulation_result = pd.DataFrame(raw_system_events)\n",
"simulation_result"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"df = simulation_result.copy()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" a simulation subset run substep timestep\n",
"0 0.000000 0 0 1 0 0\n",
"1 3.584995 0 0 1 1 1\n",
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"4 21.201066 0 0 1 1 4"
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},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['run'] == 1].head()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
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],
"text/plain": [
" a simulation subset run substep timestep\n",
"101 0.000000 0 0 2 0 0\n",
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"105 17.351503 0 0 2 1 4"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['run'] == 2].head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
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"</div>"
],
"text/plain": [
" a simulation subset run substep timestep\n",
"202 0.000000 0 0 3 0 0\n",
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]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['run'] == 3].head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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