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"""Operations for machine learning."""
import enum
import functools
import numpy as np
from . import core
from lynxkite.core import workspace
from .pytorch import pytorch_core
from lynxkite.core import ops
from tqdm import tqdm
import joblib
import pandas as pd
import pathlib
mem = joblib.Memory(".joblib-cache")
op = ops.op_registration(core.ENV)
def load_ws(model_workspace: str):
cwd = pathlib.Path()
path = cwd / model_workspace
assert path.is_relative_to(cwd), f"Path '{path}' is invalid"
assert path.exists(), f"Workspace {path} does not exist"
ws = workspace.Workspace.load(path)
return ws
@op("Define model")
def define_model(
bundle: core.Bundle,
*,
model_workspace: str,
save_as: str = "model",
):
"""Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
assert model_workspace, "Model workspace is unset."
ws = load_ws(model_workspace + ".lynxkite.json")
m = pytorch_core.build_model(ws)
m.source_workspace = model_workspace
bundle = bundle.copy()
bundle.other[save_as] = m
return bundle
# These contain the same mapping, but they get different UIs.
# For inputs, you select existing columns. For outputs, you can create new columns.
class ModelInferenceInputMapping(pytorch_core.ModelMapping):
pass
class ModelTrainingInputMapping(pytorch_core.ModelMapping):
pass
class ModelOutputMapping(pytorch_core.ModelMapping):
pass
@op("Train model")
@ops.slow
def train_model(
bundle: core.Bundle,
*,
model_name: str = "model",
input_mapping: ModelTrainingInputMapping,
epochs: int = 1,
):
"""Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
m = bundle.other[model_name].copy()
inputs = pytorch_core.to_tensors(bundle, input_mapping)
t = tqdm(range(epochs), desc="Training model")
losses = []
for _ in t:
loss = m.train(inputs)
t.set_postfix({"loss": loss})
losses.append(loss)
m.trained = True
bundle = bundle.copy()
bundle.dfs["training"] = pd.DataFrame({"training_loss": losses})
bundle.other[model_name] = m
return bundle
@op("Model inference")
@ops.slow
def model_inference(
bundle: core.Bundle,
*,
model_name: str = "model",
input_mapping: ModelInferenceInputMapping,
output_mapping: ModelOutputMapping,
):
"""Executes a trained model."""
if input_mapping is None or output_mapping is None:
return ops.Result(bundle, error="Mapping is unset.")
m = bundle.other[model_name]
assert m.trained, "The model is not trained."
inputs = pytorch_core.to_tensors(bundle, input_mapping)
outputs = m.inference(inputs)
bundle = bundle.copy()
copied = set()
for k, v in output_mapping.map.items():
if not v.df or not v.column:
continue
if v.df not in copied:
bundle.dfs[v.df] = bundle.dfs[v.df].copy()
copied.add(v.df)
bundle.dfs[v.df][v.column] = outputs[k].detach().numpy().tolist()
return bundle
@op("Train/test split")
def train_test_split(bundle: core.Bundle, *, table_name: str, test_ratio: float = 0.1):
"""Splits a dataframe in the bundle into separate "_train" and "_test" dataframes."""
df = bundle.dfs[table_name]
test = df.sample(frac=test_ratio).reset_index()
train = df.drop(test.index).reset_index()
bundle = bundle.copy()
bundle.dfs[f"{table_name}_train"] = train
bundle.dfs[f"{table_name}_test"] = test
return bundle
@op("View loss", view="visualization")
def view_loss(bundle: core.Bundle):
loss = bundle.dfs["training"].training_loss.tolist()
v = {
"title": {"text": "Training loss"},
"xAxis": {"type": "category"},
"yAxis": {"type": "value"},
"series": [{"data": loss, "type": "line"}],
}
return v
VIRIDIS = [
"#440154",
"#482777",
"#3E4989",
"#31688E",
"#26828E",
"#1F9E89",
"#35B779",
"#6CCE59",
"#B4DE2C",
"#FDE725",
]
class UMAPMetric(str, enum.Enum):
l1 = "l1"
cityblock = "cityblock"
taxicab = "taxicab"
manhattan = "manhattan"
euclidean = "euclidean"
l2 = "l2"
sqeuclidean = "sqeuclidean"
canberra = "canberra"
minkowski = "minkowski"
chebyshev = "chebyshev"
linf = "linf"
cosine = "cosine"
correlation = "correlation"
hellinger = "hellinger"
hamming = "hamming"
@op("View vectors", view="visualization")
def view_vectors(
bundle: core.Bundle,
*,
table_name: str = "nodes",
vector_column: str = "",
label_column: str = "",
n_neighbors: int = 15,
min_dist: float = 0.1,
metric: UMAPMetric = UMAPMetric.euclidean,
):
try:
from cuml.manifold.umap import UMAP
except ImportError:
from umap import UMAP
vec = np.stack(bundle.dfs[table_name][vector_column].to_numpy())
umap = functools.partial(
UMAP,
n_neighbors=n_neighbors,
min_dist=min_dist,
metric=metric.value,
)
proj = umap(n_components=2).fit_transform(vec)
color = umap(n_components=1).fit_transform(vec)
data = [[*p.tolist(), "", c.item()] for p, c in zip(proj, color)]
if label_column:
for i, row in enumerate(bundle.dfs[table_name][label_column]):
data[i][2] = row
size = 100 / len(data) ** 0.4
v = {
"title": {
"text": f"UMAP projection of {vector_column}",
},
"visualMap": {
"min": color[:, 0].min().item(),
"max": color[:, 0].max().item(),
"right": 10,
"top": "center",
"calculable": True,
"dimension": 3,
"inRange": {"color": VIRIDIS},
},
"tooltip": {"trigger": "item", "formatter": "GET_THIRD_VALUE"}
if label_column
else {"show": False},
"xAxis": [{"type": "value"}],
"yAxis": [{"type": "value"}],
"series": [{"type": "scatter", "symbolSize": size, "data": data}],
}
return v
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