Add row_count test + refactors on the final results processing + test parametrizations

CHANGELOG.md

@@ -1,18 +1,28 @@
 # Changelog:
 
+### 0.5.0 - December 18 2023
 
-### February 15, 2023
-* **Fixes:** 
-    - Package has been cleaned-up for working with Python 3.10
-### 0.4.29.1
+#### New User Features
+
+- Added toggle for enabling users to deactivate deepcopying. This is done by passing an additional object on the `ExecutionContext`, eg. `ExecutionContext(mode, additional_objs={'deepcopy_off': True})`
+
+#### New Submodules
+
+- A collection of type annotations for encapsuling `cadCAD` projects is now implemented through the `cadCAD.types` submodules
+- Added `cadCAD.tools` as a submodule, which is originated from the `cadCAD_tools` Python package. This submodule contains several helper functions for making the simulation experience more straightforward as well as a collection of performance profiling tools.
+- Added `cadCAD.diagram` as a submodule, which is originated from the `cadCAD_diagram` Python package. This submodule contains functions for programatically generating block diagrams from existing models.
+- More informative error messages when policies and SUFs are wrongly implemented. (Issues #288 and #258)
+
+#### Backend Improvements
+
+- Merged repo with the `cadCAD_legacy_devel`, which includes performance improvements. In particular, simulations will start up faster due to code optimizations.
+- `cadCAD` now uses `pytest` as the testing framework. This was made possible by isolating the existing tests and wrapping them into functions. 
 
-#### Changes
-- Parallel executor uses the context manager handling the Process Pool lifetime
+#### Fixes
 
-### 0.4.29
+- cadCAD is now Python 3.10+ compatible (Issue #306 and #301)
+- Proper support for `ExecutionMode.single_mode` (Issue #253 and #254)
 
-- Merged repo with the `cadCAD_tweaked`, which includes performance improvements
-- Python 3.10 compatible
 ### September 28, 2021
 #### New Features:
 * **ver. ≥ `0.4.28`:**

README.md

@@ -4,7 +4,7 @@
  / ___/ __` / __  / /   / /| | / / / /
 / /__/ /_/ / /_/ / /___/ ___ |/ /_/ /
 \___/\__,_/\__,_/\____/_/  |_/_____/
-by cadCAD                  ver. 0.4.28
+by cadCAD                  ver. 0.5.0
 ======================================
        Complex Adaptive Dynamics       
        o       i        e
@@ -20,7 +20,7 @@ through simulation, with support for Monte Carlo methods, A/B testing and parame
 
 # Getting Started
 
-#### Change Log: [ver. 0.4.28](CHANGELOG.md)
+#### Change Log: [ver. 0.5.0](CHANGELOG.md)
 
 [Previous Stable Release (No Longer Supported)](https://github.com/cadCAD-org/cadCAD/tree/b9cc6b2e4af15d6361d60d6ec059246ab8fbf6da)
 
@@ -47,7 +47,7 @@ $
 ## 1. Installation: 
 Requires [>= Python 3.6.13](https://www.python.org/downloads/) 
 
-**Option A:** Install Using **[pip](https://pypi.org/project/cadCAD/0.4.28/)** 
+**Option A:** Install Using **[pip](https://pypi.org/project/cadCAD/)** 
 ```bash
 pip3 install cadCAD
 ```

cadCAD/__init__.py

@@ -2,7 +2,7 @@
 from cadCAD.configuration import Experiment
 
 name = "cadCAD"
-version = "0.4.28"
+version = "0.5.0"
 experiment = Experiment()
 configs = experiment.configs
 

cadCAD/configuration/utils/__init__.py

@@ -104,7 +104,7 @@ def ep_decorator(f, y, var_dict, sub_step, sL, s, _input,  **kwargs):
         else:
             return y, s[y]
 
-    return {es: ep_decorator(f, es) for es, f in ep.items()}
+    return {es: ep_decorator(f, es) for es, f in ep.items()} # type: ignore
 
 
 def trigger_condition(s, pre_conditions, cond_opp):

cadCAD/engine/__init__.py

@@ -1,5 +1,5 @@
 from time import time
-from typing import Callable, Dict, List, Any, Tuple, Union
+from typing import Callable, Dict, List, Any, Tuple, Union, Sequence, Mapping
 from tqdm.auto import tqdm
 
 from cadCAD.utils import flatten
@@ -147,18 +147,50 @@ def get_final_results(simulations: List[StateHistory],
                               eps,
                               sessions: List[SessionDict],
                               remote_threshold: int):
+
+            # if list of lists of lists of dicts: do flatten
+            # if list of dicts: do not flatetn
+            # else raise error
+
+
+            init: bool = isinstance(simulations, Sequence)
+            failed_1 = False
+            failed_2 = False
+
+            try:
+                init: bool = isinstance(simulations, Sequence)
+                dont_flatten = init & isinstance(simulations[0], Mapping)
+                do_flatten = not dont_flatten
+            except:
+                failed_1 = True
+                do_flatten = True
+
+            try:
+                do_flatten = init & isinstance(simulations[0], Sequence)
+                do_flatten &= isinstance(simulations[0][0], Sequence)
+                do_flatten &= isinstance(simulations[0][0][0], Mapping)
+            except:
+                failed_2 = True
+                do_flatten = False
+
+            if failed_1 and failed_2:
+                raise ValueError('Invalid simulation results (Executor output is not list[dict] or list[list[list[dict]]])')
+
+
             flat_timesteps, tensor_fields = [], []
             for sim_result, psu, ep in tqdm(list(zip(simulations, psus, eps)),
                                             total=len(simulations),
                                             desc='Flattening results'):
-                flat_timesteps.append(flatten(sim_result))
+                if do_flatten:
+                    flat_timesteps.append(flatten(sim_result))
                 tensor_fields.append(create_tensor_field(psu, ep))
+
+            if do_flatten:
+                flat_simulations = flatten(flat_timesteps)
+            else:
+                flat_simulations = simulations
 
-            flat_simulations = flatten(flat_timesteps)
-            if config_amt == 1:
-                return simulations, tensor_fields, sessions
-            elif config_amt > 1:
-                return flat_simulations, tensor_fields, sessions
+            return flat_simulations, tensor_fields, sessions
 
         final_result = None
         original_N = len(configs_as_dicts(self.configs))

cadCAD/engine/execution.py

@@ -1,4 +1,4 @@
-from typing import Callable, Dict, List, Any, Tuple
+from typing import Callable, Dict, List, Any, Tuple, Sequence
 from pathos.multiprocessing import ProcessPool # type: ignore
 from collections import Counter
 from cadCAD.types import *
@@ -11,41 +11,38 @@
 
 
 def single_proc_exec(
-    simulation_execs: List[ExecutorFunction],
-    var_dict_list: List[Parameters],
-    states_lists: List[StateHistory],
-    configs_structs: List[StateUpdateBlocks],
-    env_processes_list: List[EnvProcesses],
-    Ts: List[TimeSeq],
-    SimIDs: List[SimulationID],
-    Ns: List[Run],
-    ExpIDs: List[int],
-    SubsetIDs: List[SubsetID],
-    SubsetWindows: List[SubsetWindow],
-    configured_n: List[N_Runs],
+    simulation_execs: Sequence[ExecutorFunction],
+    var_dict_list: Union[Sequence[Parameters], Parameters],
+    states_lists: Sequence[StateHistory],
+    configs_structs: Sequence[StateUpdateBlocks],
+    env_processes_list: Sequence[EnvProcesses],
+    Ts: Sequence[TimeSeq],
+    SimIDs: Sequence[SimulationID],
+    Ns: Sequence[Run],
+    ExpIDs: Sequence[int],
+    SubsetIDs: Sequence[SubsetID],
+    SubsetWindows: Sequence[SubsetWindow],
+    configured_n: Sequence[N_Runs],
     additional_objs=None
-):
+) -> List:
+
 
-    # HACK for making it run with N_Runs=1
-    if type(var_dict_list) == list:
-        var_dict_list = var_dict_list[0]
+    if not isinstance(var_dict_list, Sequence):
+        var_dict_list = list([var_dict_list])
 
-    print(f'Execution Mode: single_threaded')
-    raw_params: List[List] = [
+    raw_params = (
         simulation_execs, states_lists, configs_structs, env_processes_list,
-        Ts, SimIDs, Ns, SubsetIDs, SubsetWindows
-    ]
-    simulation_exec, states_list, config, env_processes, T, sim_id, N, subset_id, subset_window = list(
-        map(lambda x: x.pop(), raw_params)
-    )
-    result = simulation_exec(
-        var_dict_list, states_list, config, env_processes, T, sim_id, N, subset_id, subset_window, configured_n, additional_objs
-    )
-    return flatten(result)
-
-
-
-
+        Ts, SimIDs, Ns, SubsetIDs, SubsetWindows, var_dict_list)
+
+    results: List = []
+    print(f'Execution Mode: single_threaded')
+    for raw_param in zip(*raw_params):
+        simulation_exec, states_list, config, env_processes, T, sim_id, N, subset_id, subset_window, var_dict = raw_param
+        result = simulation_exec(
+            var_dict, states_list, config, env_processes, T, sim_id, N, subset_id, subset_window, configured_n, additional_objs
+        )
+        results.append(flatten(result))
+    return flatten(results)
 
 def parallelize_simulations(
     simulation_execs: List[ExecutorFunction],

cadCAD/types.py

@@ -1,4 +1,4 @@
-from typing import TypedDict, Callable, Union, Dict, List, Tuple, Iterator
+from typing import TypedDict, Callable, Union, Dict, List, Tuple, Iterable
 from collections import deque
 
 State = Dict[str, object]
@@ -20,18 +20,18 @@ class StateUpdateBlock(TypedDict):
 StateUpdateBlocks = List[StateUpdateBlock]
 
 class ConfigurationDict(TypedDict):
-    T: Iterator # Generator for the timestep variable
+    T: Iterable # Generator for the timestep variable
     N: int # Number of MC Runs
     M: Union[Parameters, SweepableParameters] # Parameters / List of Parameter to Sweep
 
 TargetValue = object
 EnvProcess: Callable[[State, SweepableParameters, TargetValue], TargetValue]
 EnvProcesses = Dict[str, Callable]
-TimeSeq = Iterator
+TimeSeq = Iterable
 SimulationID = int
 Run = int
 SubsetID = int
-SubsetWindow = Iterator
+SubsetWindow = Iterable
 N_Runs = int
 
 ExecutorFunction = Callable[[Parameters, StateHistory, StateUpdateBlocks, EnvProcesses, TimeSeq, SimulationID, Run, SubsetID, SubsetWindow, N_Runs], object]

setup.py

@@ -21,7 +21,7 @@
 """
 
 name = "cadCAD"
-version = "0.4.29"
+version = "0.5.0"
 
 setup(name=name,
       version=version,

testing/test_row_count.py

@@ -0,0 +1,67 @@
+from cadCAD.configuration import Experiment
+from cadCAD.configuration.utils import config_sim
+from cadCAD.engine import Executor, ExecutionContext, ExecutionMode
+import pytest
+
+
+CONFIG_SIGNATURES_TO_TEST = [(3, 3, 3, 3, 3), (1, 3, 3, 3, 3),
+ (3, 1, 3, 3, 3), (1, 1, 3, 3, 3),
+ (3, 3, 1, 3, 3), (1, 3, 1, 3, 3), (1, 1, 1, 3, 3)]
+
+def run_experiment(exp: Experiment, mode: str):
+    exec_context = ExecutionContext(mode)
+    executor = Executor(exec_context=exec_context, configs=exp.configs)
+    (records, tensor_field, _) = executor.execute()
+    return records
+
+
+def create_experiments(N_simulations=3, N_sweeps=3, N_runs=3, N_timesteps=3, N_substeps=3) -> Experiment:
+
+    INITIAL_STATE = {'varA': None}
+    PSUBs = [{'policies': {}, 'variables': {}}] * N_substeps
+    params = {'A': [None] * N_sweeps,
+              'B': [None]}
+
+    SIM_CONFIG = config_sim(
+        {
+            "N": N_runs,
+            "T": range(N_timesteps),
+            "M": params,  # Optional
+        }
+    )
+
+    exp = Experiment()
+    for i_sim in range(N_simulations):
+        exp.append_model(
+            sim_configs=SIM_CONFIG,
+            initial_state=INITIAL_STATE,
+            partial_state_update_blocks=PSUBs
+        )
+    return exp
+
+
+def expected_rows(N_simulations, N_sweeps, N_runs, N_timesteps, N_substeps) -> int:
+    return N_simulations * N_sweeps *  N_runs * (N_timesteps * N_substeps + 1)
+
+
+
+@pytest.mark.parametrize("N_sim,N_sw,N_r,N_t,N_s", CONFIG_SIGNATURES_TO_TEST)
+def test_row_count_single(N_sim, N_sw, N_r, N_t, N_s):
+    args = (N_sim, N_sw, N_r, N_t, N_s)
+    assert len(run_experiment(create_experiments(*args), 'single_proc')) == expected_rows(*args)
+
+
+@pytest.mark.parametrize("N_sim,N_sw,N_r,N_t,N_s", CONFIG_SIGNATURES_TO_TEST)
+def test_row_count_multi(N_sim, N_sw, N_r, N_t, N_s):
+    args = (N_sim, N_sw, N_r, N_t, N_s)
+
+    if N_sim == 1 and N_sw == 1 and N_r == 1:
+        with pytest.raises(ValueError) as e_info:
+            assert len(run_experiment(create_experiments(*args), 'multi_proc')) == expected_rows(*args)
+    else:
+        assert len(run_experiment(create_experiments(*args), 'multi_proc')) == expected_rows(*args)
+
+@pytest.mark.parametrize("N_sim,N_sw,N_r,N_t,N_s", CONFIG_SIGNATURES_TO_TEST)
+def test_row_count_local(N_sim, N_sw, N_r, N_t, N_s):
+    args = (N_sim, N_sw, N_r, N_t, N_s)
+    assert len(run_experiment(create_experiments(*args), 'local_proc')) == expected_rows(*args)