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refactor: FixedComplexity to FixedDimensionality #75

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431fac8
refactored FixedComplexity to FixedDimensionality for code and docume…
sameerashahh Jun 26, 2024
6ecefca
Merge branch 'main' into docs/FixedComplexity-to-FixedDimensionality
mkanwal Jul 10, 2024
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10 changes: 5 additions & 5 deletions README.md
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Expand Up @@ -22,15 +22,15 @@ There are three ways to interact with DynaDojo and users can take on multiple "h

![toychallenges-chips](https://github.com/FlyingWorkshop/dynadojo/assets/56043296/dcae54df-ef98-48d4-ad2e-e37c4054e67b)

`DynaDojo` comes with three off-the-shelf challenges: [Fixed Error](demos/fixed_error_demo.ipynb), [Fixed Complexity](demos/fixed_complexity_demo.ipynb), and [Fixed Train Size](demos/fixed_train_size_demo.ipynb).
`DynaDojo` comes with three off-the-shelf challenges: [Fixed Error](demos/fixed_error_demo.ipynb), [Fixed Dimensionality](demos/fixed_dimensionality_demo.ipynb), and [Fixed Train Size](demos/fixed_train_size_demo.ipynb).

```python
from dynadojo.systems import LDSSystem
from dynadojo.baselines import LinearRegression, DNN
from dynadojo.challenges import FixedComplexity
from dynadojo.challenges import FixedDimensionality
import pandas as pd

challenge = FixedComplexity(
challenge = FixedDimensionality(
N=[10, 100, 1000], # number of training examples
l=3, # latent dimension
e=3, # embed dimension
Expand All @@ -48,11 +48,11 @@ data3 = challenge.evaluate(DNN, fit_kwargs={"epochs": 20}, id="linear network")
data = pd.concat((data1, data2, data3))
challenge.plot(data)
```
<b>Out (Fixed Complexity, C = latent_dim = 5):</b>
<b>Out (Fixed Dimensionality, C = latent_dim = 5):</b>
Note how for this LDSSystem, a linear network learns more from each added sample (larger decreases in error) than a nonlinear network, and how linear regression immediately saturates at very low error. These dynamics help contextualize model performance and comparision.

<p align="center">
<img src="https://github.com/FlyingWorkshop/dynadojo/blob/main/graphics/fixed_complexity.png" alt="DynaDojo FixedComplexity Challenge Graph" />
<img src="https://github.com/FlyingWorkshop/dynadojo/blob/main/graphics/fixed_dimensionality.png" alt="DynaDojo FixedDimensionality Challenge Graph" />
</p>


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6 changes: 3 additions & 3 deletions demos/algorithms/dnn_vs_gru.ipynb
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Expand Up @@ -46,7 +46,7 @@
"from dynadojo.baselines.lr import LinearRegression\n",
"from dynadojo.baselines.dnn import DNN\n",
"from dynadojo.baselines.gru_rnn import GRU_RNN\n",
"from dynadojo.challenges import FixedComplexity\n",
"from dynadojo.challenges import FixedDimensionality\n",
"import pandas as pd"
]
},
Expand Down Expand Up @@ -75,7 +75,7 @@
}
],
"source": [
"challenge = FixedComplexity(\n",
"challenge = FixedDimensionality(\n",
" N=[10, 50, 100, 500, 1000],\n",
" l=5,\n",
" e=5,\n",
Expand Down Expand Up @@ -313,7 +313,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.14"
"version": "3.10.11"
}
},
"nbformat": 4,
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0 demos/challenges/fixed_complexity_demo.ipynb → ...hallenges/fixed_dimensionality_demo.ipynb
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38 changes: 22 additions & 16 deletions demos/fc_cnn_ca_demo.ipynb → demos/fd_cnn_ca_demo.ipynb
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0 demos/fc_dnn_nbody_demo.ipynb → demos/fd_dnn_nbody_demo.ipynb
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34 changes: 21 additions & 13 deletions demos/fts_dmd_lds_demo.ipynb
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2 changes: 1 addition & 1 deletion docs/build/html/_autosummary/dynadojo.abstractions.AbstractAlgorithm.html
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</ul>
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Expand Down Expand Up @@ -576,7 +576,7 @@ <h1>dynadojo.abstractions.AbstractChallenge<a class="headerlink" href="#dynadojo
<p>This method should return a pandas DataFrame where each row is the result of an algorithm trained and evaluated on a single system for a given training set size.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>A job is very abstractly defined, open an issue and tag &#64;carynbear or &#64;mkanwal for help. You can think of it as a trial for a single unit of computation that you wish to distribute. Sometimes we can do a single trial of multiple runs (see FixedComplexity) in special cases where the system is the same for different training set sizes.</p>
<p>A job is very abstractly defined, open an issue and tag &#64;carynbear or &#64;mkanwal for help. You can think of it as a trial for a single unit of computation that you wish to distribute. Sometimes we can do a single trial of multiple runs (see FixedDimensionality) in special cases where the system is the same for different training set sizes.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
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