Merge branch 'master' into master

.gitignore

@@ -8,7 +8,6 @@
 .eggs
 *.egg-info
 *.csv
-simulations/.ipynb_checkpoints
 monkeytype.sqlite3
 
 __pycache__
@@ -17,8 +16,4 @@ Pipfile.lock
 notebooks
 build
 
-notes.txt
-
-simulations/validation/
-testing/models/external_dataset.py
-testing/models/historical_state_access.py
+*.pickle

demos/adoption_funnel/.gitignore

@@ -0,0 +1,6 @@
+.idea
+
+src/__pycache__
+src/sim/__pycache__
+src/sim/model/__pycache__
+src/sim/model/parts/__pycache__

demos/adoption_funnel/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Matt Barlin
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

demos/adoption_funnel/README.md

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+# Adoption Funnel
+
+Building a Markov mixing process to exhibit the dynamics of an adoption process as a finite state machine.
+## State Machine
+![State Machine](images/state_machine.png)
+
+Develop a module to be utilized within other cadCAD models as a consequence of a driving signal.
+## cadCAD Specification
+![cadCAD Specification](images/adoption_cadcad_spec.png)

demos/adoption_funnel/src/sim/config.py

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+
+from cadCAD.configuration import Experiment #.append_configs
+from cadCAD.configuration.utils import config_sim
+# if test notebook is in parent above /src
+from src.sim.model.state_variables import genesis_states
+from src.sim.model.partial_state_update_block import partial_state_update_block #, partial_state_update_block_B
+from src.sim.model.sys_params import sys_params #as sys_params_A
+from src.sim.model.utils import *
+
+from copy import deepcopy
+from cadCAD import configs
+import scipy.stats as stats
+# import networkx as nx
+import numpy as np
+
+from typing import Dict, List
+
+# from .utils import *
+
+# if test notebook is in /src
+# from model.state_variables import genesis_states
+# from model.partial_state_update_block import partial_state_update_block
+# from model.sys_params import sys_params as sys_params_A
+
+from src.sim.sim_setup import SIMULATION_TIME_STEPS, MONTE_CARLO_RUNS
+
+# sys_params: Dict[str, List[int]] = sys_params
+
+sim_config = config_sim(
+    {
+        'N': MONTE_CARLO_RUNS, 
+        'T': range(SIMULATION_TIME_STEPS), # number of timesteps
+        'M': sys_params,
+    }
+)
+
+exp = Experiment()
+
+# Experiment.append_configs()
+exp.append_configs(
+    sim_configs=sim_config,
+    initial_state=genesis_states,
+    partial_state_update_blocks=partial_state_update_block
+)
+
+
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 
+# # # # # # # # # # # # # # # # # # # # GENESIS SWEEP LOGIC # # # # # # # # # # # # # # # # # # # #
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 
+
+for c in configs: # for each configuration object
+    c.initial_state = deepcopy(c.initial_state) # make a deepcopy of the initial state dict (it's shared across configs by default)
+    # for k in c.initial_state: # for each state variable
+    #     if k in c.sim_config['M']: # if there is a param with the same name in the params dict
+    #         c.initial_state[k] = c.sim_config['M'][k] # assign the param value to the initial value of the state variable
+
+    c.initial_state['pool'] = Adoption_Pool(c.sim_config['M']['SOURCE_POOL'])
+    # c.initial_state['network']  = init_network(c.initial_state['network'], c.sim_config['M'])

demos/adoption_funnel/src/sim/model/partial_state_update_block.py

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+from src.sim.model.parts.marketing_signal import *
+from src.sim.model.parts.adoption import *
+
+partial_state_update_block = [
+    {
+    # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
+    # MARKETING SIGNAL GENERATION
+        'policies': {
+            'marketing_rate': p_marketing_rate,
+            'p_marketing_shock' : p_marketing_shock,
+        },
+        'variables': {
+            'signal': s_signal,
+        }
+    },
+    {
+    # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
+    # ADOPTION STATE
+        'policies': {
+            'reputation' : p_reputation,
+            'experience' : p_experience,
+        },
+        'variables': {
+            # 'adoption': s_adoption, # AGENT BASED
+            'pool': s_pool,           # SUB POPULATION BASED
+
+        }
+    },
+
+]

demos/adoption_funnel/src/sim/model/parts/adoption.py

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+# import networkx as nx
+import numpy as np
+
+# from ..utils import *
+
+def p_reputation(params, substep, state_history, prev_state, **kwargs):
+    """
+    Policy for steady marketing spend signal generation.
+    """
+    # params = params[0]
+    constant = params['THRESHOLD']
+    random = np.random.normal(params['THRESHOLD'], scale = 1.0)
+    return {'reputation': constant}
+
+
+def p_experience(params, substep, state_history, prev_state, **kwargs):
+    """
+    Policy for steady marketing spend signal generation.
+    """
+    # params = params[0]
+    constant = params['EXO_EXPERIENCE']
+    random = np.random.normal(params['EXO_EXPERIENCE'], scale = 1.0)
+    return {'experience': constant}
+
+
+def s_adoption(params, substep, state_history, prev_state, policy_input, **kwargs):
+    """
+    State for generating signal from marketing.
+    """
+    key = 'adoption'
+
+    prev_state['adoption'].apply_signal(prev_state['signal'])
+    # value = policy_input['reputation'] + policy_input['experience']
+    prev_state['adoption'].set_threshold(params['THRESHOLD'])
+    prev_state['adoption'].determine_state(prev_state['signal'])
+    value = prev_state['adoption']
+    return (key, value)
+
+def s_pool(params, substep, state_history, prev_state, policy_input, **kwargs):
+    """
+    State for generating signal from marketing.
+    """
+    # params = params[0]
+    key = 'pool'
+
+    prev_state['pool'].apply_signal(prev_state['signal'])
+
+
+    # value = policy_input['reputation'] + policy_input['experience']
+    # FIX THIS!
+    prev_state['pool'].set_threshold(ext_threshold=params['THRESHOLD'])
+
+    prev_state['pool'].calculate_drip(params['LEAK_COEFFICIENT'])
+    # print(prev_state['pool'])
+    prev_state['pool'].update_pools(params['LEAK_COEFFICIENT'])
+    # print(prev_state['pool'])
+    # prev_state['pool'].determine_state(prev_state['signal'])
+    value = prev_state['pool']
+    return (key, value)

demos/adoption_funnel/src/sim/model/parts/marketing_signal.py

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+# import networkx as nx
+import numpy as np
+
+# from ..utils import *
+
+def p_marketing_rate(params, substep, state_history, prev_state, **kwargs):
+    # params = params[0]
+    """
+    Policy for steady marketing spend signal generation.
+    """
+    constant = params['MARKETING_STEADY']
+    random = np.random.normal(params['MARKETING_STEADY'], scale = 1.0)
+    return {'steady_signal': constant}
+
+def p_marketing_shock(params, substep, state_history, prev_state, **kwargs):
+    """
+    Policy for shock marketing (spend and other sources).
+    """
+    # Expected number of shocks
+    # coded through parameter
+    # params = params[0]
+    freq = params['MARKETING_SHOCK_FREQ'] * 0.5
+
+    # expected but randomized through poisson
+    if np.random.poisson(freq) > params['MARKETING_SHOCK_FREQ']:
+        shock = params['MARKETING_SHOCK_MAG']
+    else:
+        shock = 0
+    return {'shock_signal': shock}
+
+def s_signal(params, substep, state_history, prev_state, policy_input, **kwargs):
+    """
+    State for generating signal from marketing.
+    """
+    key = 'signal'
+
+    value = policy_input['steady_signal'] + policy_input['shock_signal']
+    return (key, value)
+
+

demos/adoption_funnel/src/sim/model/state_variables.py

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+from datetime import datetime
+# import networkx as nx
+import numpy as np
+
+# from copy import deepcopy
+# import scipy.stats as stats
+# from .sys_params import sys_params
+from src.sim.model.utils import *
+
+# Initial Values
+signal = 0
+# state = 0
+adoption = Adoption()
+# pool = Adoption_Pool() # Iniialized in config loop using source_pool parameter
+
+## Genesis States #################################################
+genesis_states = {
+    'timestamp': datetime.strptime('2020-01-01 00:00:00', '%Y-%m-%d %H:%M:%S'),
+    'signal': signal,
+    # 'adoption': adoption,  # Agent Based
+    # 'pool' : pool,  # Iniialized in config loop using source_pool parameter
+
+}

demos/adoption_funnel/src/sim/model/sys_params.py

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+# import networkx as nx
+from pprint import pprint
+
+import numpy as np
+import itertools
+
+### MARKETING PARAMETERS #################################
+# uniform distribution of marketing signalling
+MARKETING_STEADY = [500]
+
+# Noisy shock marketing signalling magnitude and expected frequency
+MARKETING_SHOCK_MAG = [1234]
+MARKETING_SHOCK_FREQ = [2]
+
+### EXTERNAL EXPERIENCE PARAMETERS #################################
+# If UX/UI are not part of the model. Can use as an external signal to
+#  generate stochastic process for experience.
+EXO_EXPERIENCE = [140]
+
+### POPULATION POOL PARAMETERS #################################
+SOURCE_POOL = [100000] #, 12]
+
+### INITIAL THRESHOLD VALUE PARAMETERS #################################
+THRESHOLD = [0.1, 1, 2, 3]
+LEAK_COEFFICIENT = [0.01]
+
+
+#### USE ONLY FOR A/B WITH PARAMETER SWEEPS OR MULTIPLE PARAMETER SWEEPS #####
+
+factors = [MARKETING_STEADY,SOURCE_POOL, THRESHOLD, LEAK_COEFFICIENT]
+product = list(itertools.product(*factors))
+MARKETING_STEADY,SOURCE_POOL, THRESHOLD, LEAK_COEFFICIENT = zip(*product)
+MARKETING_STEADY = list(MARKETING_STEADY)
+SOURCE_POOL = list(SOURCE_POOL)
+THRESHOLD = list(THRESHOLD)
+LEAK_COEFFICIENT = list(LEAK_COEFFICIENT)
+
+
+
+# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # 
+sys_params = {
+   'MARKETING_STEADY': MARKETING_STEADY,
+   'MARKETING_SHOCK_MAG': MARKETING_SHOCK_MAG, 
+   'MARKETING_SHOCK_FREQ': MARKETING_SHOCK_FREQ,
+   'EXO_EXPERIENCE': EXO_EXPERIENCE,
+   'SOURCE_POOL': SOURCE_POOL,
+   'THRESHOLD': THRESHOLD, 
+   'LEAK_COEFFICIENT': LEAK_COEFFICIENT,
+}