jeffemmett
/
cadCAD
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

renameing hell pt. 4

This commit is contained in:
Joshua E. Jodesty 2019-02-20 11:51:55 -05:00
parent 1862416b86
commit 7fb764056f
8 changed files with 141 additions and 66 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

79
README.md
View File

@ -1,10 +1,10 @@
# SimCad
# cadCAD
**Warning**:
**Do not** publish this package / software to **any** software repository **except** one permitted by BlockScience.
**Description:**
SimCAD is a differential games based simulation software package for research, validation, and Computer \
cadCAD is a differential games based simulation software package for research, validation, and Computer \
Aided Design of economic systems. An economic system is treated as a state based model and defined through a \
set of endogenous and exogenous state variables which are updated through mechanisms and environmental \
processes, respectively. Behavioral models, which may be deterministic or stochastic, provide the evolution of \
@ -15,11 +15,9 @@ Simulations may be run with a range of initial conditions and parameters for sta
and environmental processes to understand and visualize network behavior under various conditions. Support for \
A/B testing policies, monte carlo analysis and other common numerical methods is provided.
SimCAD is written in Python 3.
**1. Install Dependencies:**
```bash
pip3 install -r requirements.txt
pip install -r requirements.txt
python3 setup.py sdist bdist_wheel
pip3 install dist/*.whl
```
@ -32,51 +30,92 @@ Intructions:
Examples:
`/simulations/validation/*`
**3. Import SimCAD & Run Simulation:**
**3. Import cadCAD & Run Simulations:**
Examples: `/simulations/example_run.py` or `/simulations/example_run.ipynb`
Examples: `/simulations/*.py` or `/simulations/*.ipynb`
`/simulations/example_run.py`:
Single Simulation Run: `/simulations/single_config_run.py`
```python
import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
from validation import config1, config2
from SimCAD import configs
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import config1
from cadCAD import configs
exec_mode = ExecutionMode()
print("Simulation Execution 1")
print("Simulation Execution: Single Configuration")
print()
first_config = [configs[0]] # from config1
first_config = configs # only contains config1
single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
run1_raw_result, tensor_field = run1.main()
result = pd.DataFrame(run1_raw_result)
print()
print("Tensor Field:")
print("Tensor Field: config1")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
```
print("Simulation Execution 2: Pairwise Execution")
print()
Parameter Sweep Simulation Run (Concurrent): `/simulations/param_sweep_run.py`
```python
import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import sweep_config
from cadCAD import configs
exec_mode = ExecutionMode()
print("Simulation Execution: Concurrent Execution")
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
i = 0
config_names = ['sweep_config_A', 'sweep_config_B']
for raw_result, tensor_field in run2.main():
result = pd.DataFrame(raw_result)
print()
print("Tensor Field:")
print("Tensor Field: " + config_names[i])
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
i += 1
```
Multiple Simulation Runs (Concurrent): `/simulations/multi_config run.py`
```python
import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import config1, config2
from cadCAD import configs
exec_mode = ExecutionMode()
print("Simulation Execution: Concurrent Execution")
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
i = 0
config_names = ['config1', 'config2']
for raw_result, tensor_field in run2.main():
result = pd.DataFrame(raw_result)
print()
print("Tensor Field: " + config_names[i])
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
i =+ 1
```
The above can be run in Jupyter.
```bash
jupyter notebook
```
```

20
Simulation.md
View File

@ -1,10 +1,10 @@
# SimCAD Documentation
# cadCAD Documentation
## Introduction
A blockchain is a distributed ledger with economic agents transacting in a network. The state of the network evolves with every new transaction, which can be a result of user behaviors, protocol-defined system mechanisms, or external processes.
It is not uncommon today for blockchain projects to announce a set of rules for their network and make claims about their system level behavior. However, the validity of those claims is hardly validated. Furthermore, it is difficult to know the potential system-level impact when the network is considering an upgrade to their system rules and parameters.
It is not uncommon today for blockchain projects to announce a set of rules for their network and make claims about their system level behvaior. However, the validity of those claims is hardly validated. Furthermore, it is difficult to know the potential system-level impact when the network is considering an upgrade to their system rules and prameters.
To rigorously and reliably analyze, design, and improve cryptoeconomic networks, we are introducing this Computer Aided Design Engine where we define a cryptoeconomic network with its state and exogneous variables, model transactions as a result of agent behaviors, state mechanisms, and environmental processes. We can then run simulations with different initial states, mechanisms, environmental processes to understand and visualize network behavior under different conditions.
@ -31,13 +31,13 @@ from decimal import Decimal
import numpy as np
from datetime import timedelta
from SimCAD import configs
from SimCAD.configuration import Configuration
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
from cadCAD import configs
from cadCAD.configuration import Configuration
from cadCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
ep_time_step
```
State variables and their initial values can be defined as follows. Note that `timestamp` is a required field for this iteration of SimCAD for `env_proc` to work. Future iterations will strive to make this more generic and timestamp optional.
State variables and their initial values can be defined as follows. Note that `timestamp` is a required field for this iteration of cadCAD for `env_proc` to work. Future iterations will strive to make this more generic and timestamp optional.
```python
genesis_dict = {
's1': Decimal(0.0),
@ -63,7 +63,7 @@ transitions = {
"m2": {...}
}
```
Every behavior per transition should return a dictionary as actions taken by the agents. They will then be aggregated through addition in this version of SimCAD. Some examples of behaviors per transition are as follows. More flexible and user-defined aggregation functions will be introduced in future iterations but no example is provided at this point.
Every behavior per transition should return a dictionary as actions taken by the agents. They will then be aggregated through addition in this version of cadCAD. Some examples of behaviors per transition are as follows. More flexible and user-defined aggregation functions will be introduced in future iterations but no example is provided at this point.
```python
def b1m1(step, sL, s):
return {'param1': 1}
@ -105,7 +105,7 @@ seed = {
'c': np.random.RandomState(3)
}
```
SimCAD currently supports generating random number from a normal distribution through `bound_norm_random` with `min` and `max` values specified. Examples of environmental processes with randomness are as follows. We also define timestamp format with `ts_format` and timestamp changes with `t_delta`. Users can define other distributions to update exogenous variables.
cadCAD currently supports generating random number from a normal distribution through `bound_norm_random` with `min` and `max` values specified. Examples of environmental processes with randomness are as follows. We also define timestamp format with `ts_format` and timestamp changes with `t_delta`. Users can define other distributions to update exogenous variables.
```python
proc_one_coef_A = 0.7
proc_one_coef_B = 1.3
@ -127,7 +127,7 @@ def es5p2(step, sL, s, _input):
x = ep_time_step(s, s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
return (y, x)
```
User can also define specific external events such as market shocks at specific timestamps through `env_processes` with `proc_trigger`. An environmental process with no `proc_trigger` will be called at every timestamp. In the example below, it will return the value of `s3` at every timestamp. Logical event triggers, such as a big draw down in exogenous variables, will be supported in a later version of SimCAD.
User can also define specific external events such as market shocks at specific timestamps through `env_processes` with `proc_trigger`. An environmental process with no `proc_trigger` will be called at every timestamp. In the example below, it will return the value of `s3` at every timestamp. Logical event triggers, such as a big draw down in exogenous variables, will be supported in a later version of cadCAD.
```python
def env_a(x):
return x
@ -148,4 +148,4 @@ sim_config = {
}
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
```
```

2
requirements.txt
View File

@ -1,5 +1,5 @@
wheel
pandas
wheel
pathos
fn
tabulate

2
setup.py
View File

@ -20,4 +20,4 @@ setup(name='cadCAD',
author_email='joshua@block.science',
# license='LICENSE',
packages=find_packages()
)
)

34
simulations/example_run.py
View File

@ -1,34 +0,0 @@
import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import sweep_config #, config1, config2
from cadCAD import configs
exec_mode = ExecutionMode()
# print("Simulation Execution 1")
# print()
# first_config = [configs[0]] # FOR non-sweep configs ONLY
# single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
# run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
# run1_raw_result, tensor_field = run1.main()
# result = pd.DataFrame(run1_raw_result)
# print()
# print("Tensor Field:")
# print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
# print("Output:")
# print(tabulate(result, headers='keys', tablefmt='psql'))
# print()
print("Simulation Execution 2: Concurrent Execution")
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
for raw_result, tensor_field in run2.main():
result = pd.DataFrame(raw_result)
print()
print("Tensor Field:")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()

24
simulations/multi_config run.py Normal file
View File

@ -0,0 +1,24 @@
import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import config1, config2
from cadCAD import configs
exec_mode = ExecutionMode()
print("Simulation Execution: Concurrent Execution")
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
i = 0
config_names = ['config1', 'config2']
for raw_result, tensor_field in run2.main():
result = pd.DataFrame(raw_result)
print()
print("Tensor Field: " + config_names[i])
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
i += 1

24
simulations/param_sweep_run.py Normal file
View File

@ -0,0 +1,24 @@
import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import sweep_config
from cadCAD import configs
exec_mode = ExecutionMode()
print("Simulation Execution: Concurrent Execution")
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
i = 0
config_names = ['sweep_config_A', 'sweep_config_B']
for raw_result, tensor_field in run2.main():
result = pd.DataFrame(raw_result)
print()
print("Tensor Field: " + config_names[i])
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
i += 1

22
simulations/single_config_run.py Normal file
View File

@ -0,0 +1,22 @@
import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from cadCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import config1
from cadCAD import configs
exec_mode = ExecutionMode()
print("Simulation Execution: Single Configuration")
print()
first_config = configs # only contains config1
single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
run1_raw_result, tensor_field = run1.main()
result = pd.DataFrame(run1_raw_result)
print()
print("Tensor Field: config1")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()