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port docs and tests to new version
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@amakelov
amakelov committed Jul 2, 2024
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2 changes: 1 addition & 1 deletion README.md
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@@ -1,6 +1,6 @@
<div align="center">
<br>
<img src="../../assets/logo-no-background.png" height=128 alt="logo" align="center">
<img src="assets/logo-no-background.png" height=128 alt="logo" align="center">
<br>
<a href="#install">Install</a> |
<a href="#quickstart">Quickstart</a> |
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11 changes: 8 additions & 3 deletions docs/docs/01_storage_and_ops.md
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Expand Up @@ -12,11 +12,16 @@ it:


```python
from mandala._next.imports import Storage
from mandala.imports import Storage
import os

DB_PATH = 'my_persistent_storage.db'
if os.path.exists(DB_PATH):
os.remove(DB_PATH)

storage = Storage(
# omit for an in-memory storage
db_path='my_persistent_storage.db',
db_path=DB_PATH,
# omit to disable automatic dependency tracking & versioning
# use "__main__" to only track functions defined in the current session
deps_path='__main__',
Expand All @@ -28,7 +33,7 @@ storage = Storage(


```python
from mandala._next.imports import op
from mandala.imports import op

@op
def sum_args(a, *args, b=1, **kwargs):
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24 changes: 12 additions & 12 deletions docs/docs/02_retracing.md
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Expand Up @@ -36,7 +36,7 @@ Here's a small example of a machine learning pipeline:


```python
from mandala._next.imports import *
from mandala.imports import *
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
Expand Down Expand Up @@ -69,7 +69,7 @@ with storage:
Loading data
Training model
Getting accuracy
AtomRef(0.99, hid='d16...', cid='12a...')
AtomRef(1.0, hid='d16...', cid='b67...')


## Retracing your steps with memoization
Expand All @@ -89,8 +89,8 @@ with storage:
```

AtomRef(hid='d0f...', cid='908...', in_memory=False) AtomRef(hid='f1a...', cid='69f...', in_memory=False)
AtomRef(hid='caf...', cid='f35...', in_memory=False)
AtomRef(hid='d16...', cid='12a...', in_memory=False)
AtomRef(hid='caf...', cid='c37...', in_memory=False)
AtomRef(hid='d16...', cid='b67...', in_memory=False)


This puts all the `Ref`s along the way in your local variables (as if you've
Expand All @@ -105,7 +105,7 @@ storage.unwrap(acc)



0.99
1.0



Expand All @@ -127,17 +127,17 @@ with storage:
print(acc)
```

AtomRef(hid='d16...', cid='12a...', in_memory=False)
AtomRef(hid='d16...', cid='b67...', in_memory=False)
Training model
Getting accuracy
AtomRef(0.99, hid='6fd...', cid='12a...')
AtomRef(1.0, hid='6fd...', cid='b67...')
Loading data
Training model
Getting accuracy
AtomRef(0.82, hid='158...', cid='238...')
AtomRef(0.81, hid='158...', cid='5a4...')
Training model
Getting accuracy
AtomRef(0.9, hid='214...', cid='24c...')
AtomRef(0.84, hid='214...', cid='6c4...')


Note that the first value of `acc` from the nested loop is with
Expand Down Expand Up @@ -165,8 +165,8 @@ with storage:
print(n_class, n_estimators, storage.unwrap(acc))
```

2 5 0.99
2 10 0.99
2 5 1.0
2 10 1.0


## Memoized code as storage interface
Expand All @@ -185,5 +185,5 @@ with storage:
print(storage.unwrap(acc), storage.unwrap(model))
```

0.82 RandomForestClassifier(max_depth=2, n_estimators=5)
0.81 RandomForestClassifier(max_depth=2, n_estimators=5)

36 changes: 18 additions & 18 deletions docs/docs/03_cf.md
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Expand Up @@ -50,7 +50,7 @@ from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

from mandala._next.imports import *
from mandala.imports import *
try:
import rich
from rich import print as pprint
Expand Down Expand Up @@ -162,8 +162,8 @@ cf.draw(verbose=True)
Computational graph:
X_train@output_0, X_test@output_1, y_train@output_2, y_test@output_3 =
<span style="color: #800080; text-decoration-color: #800080; font-weight: bold">generate_dataset</span><span style="font-weight: bold">(</span><span style="color: #808000; text-decoration-color: #808000">random_seed</span>=<span style="color: #800080; text-decoration-color: #800080">random_seed</span><span style="font-weight: bold">)</span>
model@output_0 = <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">train_model</span><span style="font-weight: bold">(</span><span style="color: #808000; text-decoration-color: #808000">X_train</span>=<span style="color: #800080; text-decoration-color: #800080">X_train</span>, <span style="color: #808000; text-decoration-color: #808000">n_estimators</span>=<span style="color: #800080; text-decoration-color: #800080">n_estimators</span>, <span style="color: #808000; text-decoration-color: #808000">y_train</span>=<span style="color: #800080; text-decoration-color: #800080">y_train</span><span style="font-weight: bold">)</span>
v@output_0 = <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">eval_model</span><span style="font-weight: bold">(</span><span style="color: #808000; text-decoration-color: #808000">y_test</span>=<span style="color: #800080; text-decoration-color: #800080">y_test</span>, <span style="color: #808000; text-decoration-color: #808000">X_test</span>=<span style="color: #800080; text-decoration-color: #800080">X_test</span>, <span style="color: #808000; text-decoration-color: #808000">model</span>=<span style="color: #800080; text-decoration-color: #800080">model</span><span style="font-weight: bold">)</span>
model@output_0 = <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">train_model</span><span style="font-weight: bold">(</span><span style="color: #808000; text-decoration-color: #808000">X_train</span>=<span style="color: #800080; text-decoration-color: #800080">X_train</span>, <span style="color: #808000; text-decoration-color: #808000">y_train</span>=<span style="color: #800080; text-decoration-color: #800080">y_train</span>, <span style="color: #808000; text-decoration-color: #808000">n_estimators</span>=<span style="color: #800080; text-decoration-color: #800080">n_estimators</span><span style="font-weight: bold">)</span>
v@output_0 = <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">eval_model</span><span style="font-weight: bold">(</span><span style="color: #808000; text-decoration-color: #808000">X_test</span>=<span style="color: #800080; text-decoration-color: #800080">X_test</span>, <span style="color: #808000; text-decoration-color: #808000">y_test</span>=<span style="color: #800080; text-decoration-color: #800080">y_test</span>, <span style="color: #808000; text-decoration-color: #808000">model</span>=<span style="color: #800080; text-decoration-color: #800080">model</span><span style="font-weight: bold">)</span>
</pre>


Expand Down Expand Up @@ -225,7 +225,7 @@ cf.draw(verbose=True)
<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">5</span> <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">variable</span><span style="font-weight: bold">(</span>s<span style="font-weight: bold">)</span> <span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">14</span> unique refs<span style="font-weight: bold">)</span>
<span style="color: #008080; text-decoration-color: #008080; font-weight: bold">1</span> <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">operation</span><span style="font-weight: bold">(</span>s<span style="font-weight: bold">)</span> <span style="font-weight: bold">(</span><span style="color: #008080; text-decoration-color: #008080; font-weight: bold">4</span> unique calls<span style="font-weight: bold">)</span>
Computational graph:
output_0@output_0, output_1@output_1 = <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">train_model</span><span style="font-weight: bold">(</span><span style="color: #808000; text-decoration-color: #808000">X_train</span>=<span style="color: #800080; text-decoration-color: #800080">X_train</span>, <span style="color: #808000; text-decoration-color: #808000">n_estimators</span>=<span style="color: #800080; text-decoration-color: #800080">n_estimators</span>, <span style="color: #808000; text-decoration-color: #808000">y_train</span>=<span style="color: #800080; text-decoration-color: #800080">y_train</span><span style="font-weight: bold">)</span>
output_0@output_0, output_1@output_1 = <span style="color: #800080; text-decoration-color: #800080; font-weight: bold">train_model</span><span style="font-weight: bold">(</span><span style="color: #808000; text-decoration-color: #808000">X_train</span>=<span style="color: #800080; text-decoration-color: #800080">X_train</span>, <span style="color: #808000; text-decoration-color: #808000">y_train</span>=<span style="color: #800080; text-decoration-color: #800080">y_train</span>, <span style="color: #808000; text-decoration-color: #808000">n_estimators</span>=<span style="color: #800080; text-decoration-color: #800080">n_estimators</span><span style="font-weight: bold">)</span>
</pre>


Expand All @@ -246,13 +246,13 @@ print(cf.df(values='refs').to_markdown())
```

Extracting tuples from the computation graph:
output_0@output_0, output_1@output_1 = train_model(n_estimators=n_estimators, y_train=y_train, X_train=X_train)
| | y_train | X_train | n_estimators | train_model | output_1 | output_0 |
output_0@output_0, output_1@output_1 = train_model(y_train=y_train, X_train=X_train, n_estimators=n_estimators)
| | X_train | n_estimators | y_train | train_model | output_0 | output_1 |
|---:|:-----------------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|:----------------------------------------------|:-----------------------------------------------------|:-----------------------------------------------------|
| 0 | AtomRef(hid='faf...', cid='83f...', in_memory=False) | AtomRef(hid='efa...', cid='a6d...', in_memory=False) | AtomRef(hid='9fd...', cid='4ac...', in_memory=False) | Call(train_model, cid='5af...', hid='514...') | AtomRef(hid='784...', cid='238...', in_memory=False) | AtomRef(hid='331...', cid='e64...', in_memory=False) |
| 1 | AtomRef(hid='faf...', cid='83f...', in_memory=False) | AtomRef(hid='efa...', cid='a6d...', in_memory=False) | AtomRef(hid='235...', cid='c04...', in_memory=False) | Call(train_model, cid='204...', hid='c55...') | AtomRef(hid='5b7...', cid='f0a...', in_memory=False) | AtomRef(hid='208...', cid='c75...', in_memory=False) |
| 2 | AtomRef(hid='faf...', cid='83f...', in_memory=False) | AtomRef(hid='efa...', cid='a6d...', in_memory=False) | AtomRef(hid='120...', cid='9bc...', in_memory=False) | Call(train_model, cid='3be...', hid='e60...') | AtomRef(hid='646...', cid='acb...', in_memory=False) | AtomRef(hid='522...', cid='d5a...', in_memory=False) |
| 3 | AtomRef(hid='faf...', cid='83f...', in_memory=False) | AtomRef(hid='efa...', cid='a6d...', in_memory=False) | AtomRef(hid='98c...', cid='29d...', in_memory=False) | Call(train_model, cid='c4f...', hid='5f7...') | AtomRef(hid='760...', cid='46b...', in_memory=False) | AtomRef(hid='b25...', cid='462...', in_memory=False) |
| 0 | AtomRef(hid='efa...', cid='a6d...', in_memory=False) | AtomRef(hid='120...', cid='9bc...', in_memory=False) | AtomRef(hid='faf...', cid='83f...', in_memory=False) | Call(train_model, cid='3be...', hid='e60...') | AtomRef(hid='522...', cid='d5a...', in_memory=False) | AtomRef(hid='646...', cid='acb...', in_memory=False) |
| 1 | AtomRef(hid='efa...', cid='a6d...', in_memory=False) | AtomRef(hid='9fd...', cid='4ac...', in_memory=False) | AtomRef(hid='faf...', cid='83f...', in_memory=False) | Call(train_model, cid='5af...', hid='514...') | AtomRef(hid='331...', cid='e64...', in_memory=False) | AtomRef(hid='784...', cid='238...', in_memory=False) |
| 2 | AtomRef(hid='efa...', cid='a6d...', in_memory=False) | AtomRef(hid='235...', cid='c04...', in_memory=False) | AtomRef(hid='faf...', cid='83f...', in_memory=False) | Call(train_model, cid='204...', hid='c55...') | AtomRef(hid='208...', cid='c75...', in_memory=False) | AtomRef(hid='5b7...', cid='f0a...', in_memory=False) |
| 3 | AtomRef(hid='efa...', cid='a6d...', in_memory=False) | AtomRef(hid='98c...', cid='29d...', in_memory=False) | AtomRef(hid='faf...', cid='83f...', in_memory=False) | Call(train_model, cid='c4f...', hid='5f7...') | AtomRef(hid='b25...', cid='462...', in_memory=False) | AtomRef(hid='760...', cid='46b...', in_memory=False) |


##
Expand Down Expand Up @@ -496,14 +496,14 @@ print(cf.df().drop(columns=['X_train', 'y_train']).to_markdown())

Extracting tuples from the computation graph:
X_train@output_0, y_train@output_2 = generate_dataset(random_seed=random_seed)
output_0@output_0, output_1@output_1 = train_model(n_estimators=n_estimators, y_train=y_train, X_train=X_train)
output_0@output_0, output_1@output_1 = train_model(y_train=y_train, X_train=X_train, n_estimators=n_estimators)
output_0_0@output_0 = eval_model(model=output_0)
| | random_seed | generate_dataset | n_estimators | train_model | output_1 | output_0 | eval_model | output_0_0 |
|---:|--------------:|:---------------------------------------------------|---------------:|:----------------------------------------------|-----------:|:-----------------------------------------------------|:---------------------------------------------|-------------:|
| 0 | 42 | Call(generate_dataset, cid='19a...', hid='c3f...') | 80 | Call(train_model, cid='3be...', hid='e60...') | 0.83 | RandomForestClassifier(max_depth=2, n_estimators=80) | Call(eval_model, cid='137...', hid='d32...') | 0.82 |
| 1 | 42 | Call(generate_dataset, cid='19a...', hid='c3f...') | 20 | Call(train_model, cid='204...', hid='c55...') | 0.8 | RandomForestClassifier(max_depth=2, n_estimators=20) | | nan |
| 2 | 42 | Call(generate_dataset, cid='19a...', hid='c3f...') | 40 | Call(train_model, cid='5af...', hid='514...') | 0.82 | RandomForestClassifier(max_depth=2, n_estimators=40) | Call(eval_model, cid='38f...', hid='5d3...') | 0.81 |
| 3 | 42 | Call(generate_dataset, cid='19a...', hid='c3f...') | 10 | Call(train_model, cid='c4f...', hid='5f7...') | 0.74 | RandomForestClassifier(max_depth=2, n_estimators=10) | | nan |
| | n_estimators | random_seed | generate_dataset | train_model | output_0 | eval_model | output_0_0 | output_1 |
|---:|---------------:|--------------:|:---------------------------------------------------|:----------------------------------------------|:-----------------------------------------------------|:---------------------------------------------|-------------:|-----------:|
| 0 | 10 | 42 | Call(generate_dataset, cid='19a...', hid='c3f...') | Call(train_model, cid='c4f...', hid='5f7...') | RandomForestClassifier(max_depth=2, n_estimators=10) | | nan | 0.74 |
| 1 | 40 | 42 | Call(generate_dataset, cid='19a...', hid='c3f...') | Call(train_model, cid='5af...', hid='514...') | RandomForestClassifier(max_depth=2, n_estimators=40) | Call(eval_model, cid='38f...', hid='5d3...') | 0.81 | 0.82 |
| 2 | 20 | 42 | Call(generate_dataset, cid='19a...', hid='c3f...') | Call(train_model, cid='204...', hid='c55...') | RandomForestClassifier(max_depth=2, n_estimators=20) | | nan | 0.8 |
| 3 | 80 | 42 | Call(generate_dataset, cid='19a...', hid='c3f...') | Call(train_model, cid='3be...', hid='e60...') | RandomForestClassifier(max_depth=2, n_estimators=80) | Call(eval_model, cid='137...', hid='d32...') | 0.82 | 0.83 |


Importantly, we see that some computations only partially follow the full
Expand Down Expand Up @@ -554,7 +554,7 @@ cf.print_graph()
```

X_train@output_0, y_train@output_2 = generate_dataset(random_seed=random_seed)
output_0@output_0, output_1@output_1 = train_model(X_train=X_train, n_estimators=n_estimators, y_train=y_train)
output_0@output_0, output_1@output_1 = train_model(X_train=X_train, y_train=y_train, n_estimators=n_estimators)
output_0_0@output_0 = eval_model(model=output_0)


Expand Down
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