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Validation data generation, removal of loss function overrides #234

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ba5be8d
Removed resetting of loss in fit of ModnetModel.
kaueltzen Oct 25, 2024
235d2ea
Added function for creating shuffled, (stratified) validation data.
kaueltzen Oct 25, 2024
8ad5ddd
Replaced train_test_split in fit_preset and FitGenetic with custom fu…
kaueltzen Oct 25, 2024
2c21ce2
Added custom validation data generation to DeprecaedMODNetModel.
kaueltzen Oct 28, 2024
2f9fcd6
Added stratification column index.
kaueltzen Oct 28, 2024
b5388a6
Merge branch 'ppdebreuck:master' into splitting
kaueltzen Nov 18, 2024
61a8821
Removed determination of target for stratification, using always firs…
kaueltzen Nov 18, 2024
eaa5f6e
Type changes
kaueltzen Nov 18, 2024
0b9cbd3
Moved generate_shuffled_and_stratified_val_split to modnet.utils, cor…
kaueltzen Dec 18, 2024
0d11052
Merge branch 'master' into splitting
kaueltzen Dec 18, 2024
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14 changes: 9 additions & 5 deletions modnet/hyper_opt/fit_genetic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,10 +4,12 @@
from typing import List, Optional, Dict, Union, Callable
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
from modnet.preprocessing import MODData
from modnet.models import MODNetModel, EnsembleMODNetModel
from modnet.utils import LOG
from modnet.models import (
MODNetModel,
EnsembleMODNetModel,
)
from modnet.utils import LOG, generate_shuffled_and_stratified_val_split
import multiprocessing
import tqdm

Expand Down Expand Up @@ -457,8 +459,10 @@ def function_fitness(
)
if not nested:
splits = [
train_test_split(
range(len(self.train_data.df_featurized)), test_size=val_fraction
generate_shuffled_and_stratified_val_split(
y=self.train_data.df_targets.values,
val_fraction=val_fraction,
classification=max(self.num_classes.values()) >= 2,
)
]
n_splits = 1
Expand Down
6 changes: 5 additions & 1 deletion modnet/models/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,4 +14,8 @@

from .ensemble import EnsembleMODNetModel

__all__ = ("MODNetModel", "BayesianMODNetModel", "EnsembleMODNetModel")
__all__ = (
"MODNetModel",
"BayesianMODNetModel",
"EnsembleMODNetModel",
)
13 changes: 9 additions & 4 deletions modnet/models/ensemble.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,11 +13,12 @@
import tensorflow as tf

from sklearn.utils import resample
from sklearn.model_selection import train_test_split

from modnet.models.vanilla import MODNetModel
from modnet.models.vanilla import (
MODNetModel,
)
from modnet import __version__
from modnet.utils import LOG
from modnet.utils import LOG, generate_shuffled_and_stratified_val_split
from modnet.preprocessing import MODData

__all__ = ("EnsembleMODNetModel",)
Expand Down Expand Up @@ -323,7 +324,11 @@ def fit_preset(
)
if not nested:
splits = [
train_test_split(range(len(data.df_featurized)), test_size=val_fraction)
generate_shuffled_and_stratified_val_split(
y=data.df_targets.values,
val_fraction=val_fraction,
classification=classification,
)
]
n_splits = 1
else:
Expand Down
54 changes: 45 additions & 9 deletions modnet/models/vanilla.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,19 +13,18 @@
import numpy as np
import warnings
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error, mean_squared_error, roc_auc_score
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
import tensorflow as tf

from modnet.preprocessing import MODData
from modnet.utils import LOG
from modnet.utils import LOG, generate_shuffled_and_stratified_val_split
from modnet import __version__

import tqdm

__all__ = ("MODNetModel",)
__all__ = "MODNetModel"


class MODNetModel:
Expand Down Expand Up @@ -401,20 +400,24 @@ def fit(
targ = prop[0]
if self.multi_label:
y_inner = np.stack(val_data.df_targets[targ].values)
if loss is None:
loss = "binary_crossentropy"
else:
y_inner = tf.keras.utils.to_categorical(
val_data.df_targets[targ].values,
num_classes=self.num_classes[targ],
)
loss = "categorical_crossentropy"
else:
y_inner = val_data.df_targets[prop].values.astype(
np.float64, copy=False
)
val_y.append(y_inner)
validation_data = (val_x, val_y)
elif val_fraction > 0:
x, y, validation_data = generate_shuffled_and_stratified_val_data(
x=x,
y=y,
val_fraction=val_fraction,
classification=max(self.num_classes.values()) >= 2,
)
else:
validation_data = None

Expand All @@ -427,7 +430,7 @@ def fit(

# Optionally set up print callback
if verbose:
if val_fraction > 0 or validation_data:
if validation_data:
if self._multi_target and val_key is not None:
val_metric_key = f"val_{val_key}_mae"
else:
Expand Down Expand Up @@ -577,7 +580,11 @@ def fit_preset(
)
if not nested:
splits = [
train_test_split(range(len(data.df_featurized)), test_size=val_fraction)
generate_shuffled_and_stratified_val_split(
y=data.df_targets.values,
val_fraction=val_fraction,
classification=classification,
)
]
n_splits = 1
else:
Expand Down Expand Up @@ -1297,6 +1304,13 @@ def fit(
)
val_y.append(y_inner)
validation_data = (val_x, val_y)
elif val_fraction > 0:
x, y, validation_data = generate_shuffled_and_stratified_val_data(
x=x,
y=y,
val_fraction=val_fraction,
classification=max(self.num_classes.values()) >= 2,
)
else:
validation_data = None

Expand All @@ -1307,7 +1321,7 @@ def fit(

# Optionally set up print callback
if verbose:
if val_fraction > 0 or validation_data:
if validation_data:
if self._multi_target and val_key is not None:
val_metric_key = f"val_{val_key}_mae"
else:
Expand Down Expand Up @@ -1537,3 +1551,25 @@ def validate_model(

def map_validate_model(kwargs):
return validate_model(**kwargs)


def generate_shuffled_and_stratified_val_data(
x: np.ndarray,
y: list[np.ndarray],
val_fraction: float,
classification: bool,
):
"""
Generate train and validation data that is shuffled, reproducible and, if classification, stratified.
Please note for classification tasks that stratification is performed on first target.
y: list of 2D array with combined dimensions of (n_targets, n_samples, 1 or n_classes)
"""
y_split = np.array([y[0]]).swapaxes(1, 0)
train_idx, val_idx = generate_shuffled_and_stratified_val_split(
y=y_split, val_fraction=val_fraction, classification=classification
)
return (
x[train_idx],
[t[train_idx] for t in y],
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@ppdebreuck ppdebreuck Dec 18, 2024

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This doesn't seem correct ? y[train_idx] should work, right?. It would pick the wrong columns now.

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@kaueltzen kaueltzen Dec 18, 2024

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The y of MODNetModel.fit() and of generate_shuffled_and_stratified_val_data has the first two dimensions (n_target_groups, n_samples) while the y of generate_shuffled_and_stratified_val_split that is used elsewhere (data.df_targets.values) has the first 2 dimensions (n_samples, n_targets).

(x[val_idx], [t[val_idx] for t in y]),
)
6 changes: 3 additions & 3 deletions modnet/tests/test_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ def test_train_small_model_single_target(subset_moddata, tf_session):
n_feat=10,
)

model.fit(data, epochs=2)
model.fit(data, epochs=2, val_fraction=0.15)
model.predict(data)
assert not np.isnan(model.evaluate(data))

Expand Down Expand Up @@ -50,7 +50,7 @@ def is_metal(egap):
n_feat=10,
)

model.fit(data, epochs=2)
model.fit(data, epochs=2, val_fraction=0.15)
assert not np.isnan(model.evaluate(data))


Expand All @@ -71,7 +71,7 @@ def test_train_small_model_multi_target(subset_moddata, tf_session):
n_feat=10,
)

model.fit(data, epochs=2)
model.fit(data, epochs=2, val_fraction=0.15)
model.predict(data)
assert not np.isnan(model.evaluate(data))

Expand Down
28 changes: 28 additions & 0 deletions modnet/utils.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,6 @@
import logging
import numpy as np
from sklearn.model_selection import train_test_split
import sys

LOG = logging.getLogger("modnet")
Expand Down Expand Up @@ -28,3 +30,29 @@ def get_hash_of_file(fname, algo="sha512"):
fb = f.read(block_size)

return _hash.hexdigest()


def generate_shuffled_and_stratified_val_split(
y: np.ndarray, val_fraction: float, classification: bool
):
"""
Generate train validation split that is shuffled, reproducible and, if classification, stratified.
Please note for classification tasks that stratification is performed on first target.
y: np.ndarray (n_samples, n_targets) or (n_samples, n_targets, n_classes)
"""
if classification:
if isinstance(y[0][0], list) or isinstance(y[0][0], np.ndarray):
ycv = np.argmax(y[:, 0], axis=1)
else:
ycv = y[:, 0]
return train_test_split(
range(len(y)),
test_size=val_fraction,
random_state=42,
shuffle=True,
stratify=ycv,
)
else:
return train_test_split(
range(len(y)), test_size=val_fraction, random_state=42, shuffle=True
)
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