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Lines changed: 186 additions & 125 deletions

‎cebra_lens/__init__.py‎

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@@ -1,14 +1,14 @@
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# example of structure so that you can directly use the functions get_layer_activations instead of having to do CEBRA_Lens.activations.get_layer_activations
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from .activations import *
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from .matplotlib import *
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from .quantification.cka_metric import *
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from .quantification.decoder import *
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from .quantification.distance import *
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from .quantification.cka_metric import *
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from .quantification.rdm_metric import *
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from .quantification.tsne import *
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from .matplotlib import *
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from .utils import *
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from .utils_allen import *
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from .utils_hpc import *
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from .utils import *
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# selects what files can be imported when doing from CEBRA_Lens import * --> keep env clean
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# __all__ = ['get_layer_activations']

‎cebra_lens/activations.py‎

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@@ -1,13 +1,15 @@
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"""Functions to retrieve and handle layer activations"""
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from typing import Dict, List, Optional, Tuple, Type
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import cebra
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import torch
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import torch.nn as nn
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import matplotlib.pyplot as plt
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import numpy as np
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import numpy.typing as npt
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from typing import Tuple, Dict, List, Type, Optional
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import torch
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import torch.nn as nn
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from .matplotlib import plot_activations
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import matplotlib.pyplot as plt
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def _cut_array(array: npt.NDArray,

‎cebra_lens/matplotlib.py‎

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@@ -1,14 +1,15 @@
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"""Matplotlib interface to CEBRA-Lens."""
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import random
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from abc import *
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from typing import Optional, Tuple, List, Dict, Union
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import seaborn as sns
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from typing import Dict, List, Optional, Tuple, Union
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import matplotlib.axes
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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import numpy.typing as npt
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import random
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import seaborn as sns
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import torch
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class _BasePlot:

‎cebra_lens/quantification/__init__.py‎

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@@ -1,7 +1,7 @@
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from .base import *
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from .cka_metric import *
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from .rdm_metric import *
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from .misc import *
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from .distance import *
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from .decoder import *
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from .base import *
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from .distance import *
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from .misc import *
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from .rdm_metric import *
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from .tsne import *

‎cebra_lens/quantification/base.py‎

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@@ -1,11 +1,12 @@
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from tqdm import tqdm
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import numpy as np
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import pickle
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import types
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from typing import List, Union, Dict
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from abc import *
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from pathlib import Path
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from typing import Dict, List, Union
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import numpy as np
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import numpy.typing as npt
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from tqdm import tqdm
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class _BaseMetric:

‎cebra_lens/quantification/cka_metric.py‎

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@@ -5,13 +5,16 @@
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"""
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8-
from tqdm import tqdm
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from typing import Dict, List, Optional, Tuple
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import matplotlib
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import numpy as np
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from .base import _BaseMetric
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import cebra_lens.matplotlib as cebra_lens_matplotlib
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from typing import Optional, List, Dict, Tuple
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import numpy.typing as npt
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import matplotlib
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from tqdm import tqdm
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import cebra_lens.matplotlib as cebra_lens_matplotlib
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from .base import _BaseMetric
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class CKA(_BaseMetric):
@@ -189,7 +192,7 @@ def _compute_per_layer(
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cka_matrix = np.zeros((len(embeddings_1), len(embeddings_1[0])))
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for j in tqdm(range(len(embeddings_1))):
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if flag:
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# the situation when there multiple models inside model labels and the same number of
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# the situation when there multiple models inside model labels and the same number of
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# models inside each label
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cka_matrix[j, :] = self._compute_cka(embeddings_1[j],
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embeddings_2[j])
@@ -230,7 +233,7 @@ def compute(self, activations: Dict[str, npt.NDArray],
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if len(activations_1) != len(activations_2):
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# if the number of models in a label is different from the other model label
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# choose embeddings_1 for the one with more models, and then embeddings_2 just compare with
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# choose embeddings_1 for the one with more models, and then embeddings_2 just compare with
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# the first model
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if len(activations_1) > len(activations_2):
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embeddings_1 = activations_1

‎cebra_lens/quantification/decoder.py‎

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@@ -1,17 +1,20 @@
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from typing import Dict, Optional, Tuple, Type
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import cebra
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import torch
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import matplotlib
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import numpy as np
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from ..utils_allen import decoding_frames
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from ..utils_hpc import decoding_pos_dir
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from ..activations import get_activations_model
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from .base import _BaseMetric
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import cebra_lens.matplotlib as cebra_lens_matplotlib
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import numpy.typing as npt
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from typing import Dict, Type, Tuple, Optional
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import torch.nn as nn
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import sklearn.metrics
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import torch
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import torch as pt
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import matplotlib
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import torch.nn as nn
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import cebra_lens.matplotlib as cebra_lens_matplotlib
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from ..activations import get_activations_model
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from ..utils_allen import decoding_frames
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from ..utils_hpc import decoding_pos_dir
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from .base import _BaseMetric
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def decoding(
@@ -370,8 +373,10 @@ def plot(
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)
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if self.output_only:
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return cebra_lens_matplotlib.plot_decoding(results_dict, palette, self.dataset_label,
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label, plot_error, ax)
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return cebra_lens_matplotlib.plot_decoding(results_dict, palette,
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self.dataset_label,
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label, plot_error, ax)
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else:
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return cebra_lens_matplotlib.plot_layer_decoding(results_dict, title, self.dataset_label,
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label, plot_error, figsize)
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return cebra_lens_matplotlib.plot_layer_decoding(
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results_dict, title, self.dataset_label, label, plot_error,
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figsize)

‎cebra_lens/quantification/distance.py‎

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"file containing all the functions relative to distance computing"
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from typing import Dict, List, Optional, Tuple, Union
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import numpy as np
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import numpy.typing as npt
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from scipy.spatial.distance import cdist, pdist
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from sklearn.preprocessing import StandardScaler
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from typing import List, Optional, Tuple, Union, Dict
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from .misc import discrete_binning, repetition_binning, continuous_binning
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from .base import _BaseMetric
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910
from ..matplotlib import *
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import numpy.typing as npt
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from ..utils import extract_label
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from .base import _BaseMetric
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from .misc import continuous_binning, discrete_binning, repetition_binning
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class DistanceMetric:

‎cebra_lens/quantification/misc.py‎

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@@ -1,11 +1,12 @@
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"""misc functions like normalization and possibly others"""
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3+
import warnings
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from random import sample
5+
from typing import List
6+
47
import numpy as np
5-
import torch
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import numpy.typing as npt
7-
from typing import List
8-
import warnings
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import torch
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def normalize_minmax(rdm: npt.NDArray) -> npt.NDArray:

‎cebra_lens/quantification/rdm_metric.py‎

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"""All the functions relative to the Representation Dissimilarity Matrix (RDM) calculation"""
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from typing import Dict, List, Optional
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from typing import Dict, List, Optional, Tuple, Union
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5+
import matplotlib
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import numpy as np
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import numpy.typing as npt
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import torch
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from scipy.linalg import block_diag
6-
from typing import List, Optional, Tuple, Union
710
from scipy.spatial.distance import correlation, pdist, squareform
8-
from .misc import discrete_binning, continuous_binning
9-
import torch
10-
import matplotlib
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from .base import _BaseMetric
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1212
import cebra_lens.matplotlib as cebra_lens_matplotlib
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import numpy.typing as npt
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14+
from .base import _BaseMetric
15+
from .misc import continuous_binning, discrete_binning
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class RDM(_BaseMetric):

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