import numpy as np
-[docs]def transform_distances_to_scores(distances: np.ndarray, k: int, t: int) -> np.ndarray:
+[docs]def transform_distances_to_scores(
+ avg_distances: np.ndarray, t: int, scaling_factor: float
+) -> np.ndarray:
"""Returns an outlier score for each example based on its average distance to its k nearest neighbors.
The transformation of a distance, :math:`d` , to a score, :math:`o` , is based on the following formula:
@@ -561,18 +563,21 @@ Source code for cleanlab.internal.outlier
Parameters
----------
- distances : np.ndarray
- An array of distances of shape ``(N, num_neighbors)``, where N is the number of examples.
- Each row contains the distances to each example's `num_neighbors` nearest neighbors.
- It is assumed that each row is sorted in ascending order.
-
- k : int
- Number of neighbors used to compute the average distance to each example.
- This assumes that the second dimension of distances is k or greater, but it
- uses slicing to avoid indexing errors.
+ avg_distances : np.ndarray
+ An array of distances of shape ``(N)``, where N is the number of examples.
+ Each entry represents an example's average distance to its k nearest neighbors.
t : int
- Controls transformation of distances between examples into similarity scores that lie in [0,1].
+ A sensitivity parameter that modulates the strength of the transformation from distances to scores.
+ Higher values of `t` result in more pronounced differentiation between the scores of examples
+ lying in the range [0,1].
+
+ scaling_factor : float
+ A scaling factor used to normalize the distances before they are converted into scores. A valid
+ scaling factor is any positive number. The choice of scaling factor should be based on the
+ distribution of distances between neighboring examples. A good rule of thumb is to set the
+ scaling factor to the median distance between neighboring examples. A lower scaling factor
+ results in more pronounced differentiation between the scores of examples lying in the range [0,1].
Returns
-------
@@ -585,14 +590,12 @@ Source code for cleanlab.internal.outlier
>>> from cleanlab.outlier import transform_distances_to_scores
>>> distances = np.array([[0.0, 0.1, 0.25],
... [0.15, 0.2, 0.3]])
- >>> transform_distances_to_scores(distances, k=2, t=1)
- array([0.95122942, 0.83945702])
+ >>> avg_distances = np.mean(distances, axis=1)
+ >>> transform_distances_to_scores(avg_distances, t=1, scaling_factor=1)
+ array([0.88988177, 0.80519832])
"""
- # Calculate average distance to k-nearest neighbors
- avg_knn_distances = distances[:, :k].mean(axis=1)
-
# Map ood_features_scores to range 0-1 with 0 = most concerning
- ood_features_scores: np.ndarray = np.exp(-1 * avg_knn_distances * t)
+ ood_features_scores: np.ndarray = np.exp(-1 * avg_distances / scaling_factor * t)
return ood_features_scores
diff --git a/master/_modules/cleanlab/outlier.html b/master/_modules/cleanlab/outlier.html
index 87f2ab850..f7b9c173b 100644
--- a/master/_modules/cleanlab/outlier.html
+++ b/master/_modules/cleanlab/outlier.html
@@ -553,7 +553,7 @@ Source code for cleanlab.outlier
from cleanlab.count import get_confident_thresholds
from sklearn.neighbors import NearestNeighbors
from sklearn.exceptions import NotFittedError
-from typing import Optional, Union, Tuple, Dict, cast
+from typing import Optional, Union, Tuple, Dict
from cleanlab.internal.label_quality_utils import (
_subtract_confident_thresholds,
get_normalized_entropy,
@@ -647,6 +647,9 @@ Source code for cleanlab.outlier
"To use GEN, we recommend setting: params['adjust_pred_probs'] = False"
)
+ # scaling_factor internally used to rescale distances based on mean distances to k nearest neighbors
+ self.params["scaling_factor"] = None
+
[docs] def fit_score(
self,
*,
@@ -794,7 +797,9 @@ Source code for cleanlab.outlier
raise ValueError(
"OOD estimator needs to be fit on features first. Call `fit()` or `fit_scores()` before this function."
)
- scores, _ = _get_ood_features_scores(features, **self._get_params(self.OUTLIER_PARAMS))
+ scores, _ = self._get_ood_features_scores(
+ features, **self._get_params(self.OUTLIER_PARAMS)
+ )
if pred_probs is not None:
if self.params["confident_thresholds"] is None and self.params["adjust_pred_probs"]:
@@ -866,7 +871,7 @@ Source code for cleanlab.outlier
# Get ood features scores
if verbose:
print("Fitting OOD estimator based on provided features ...")
- scores, knn = _get_ood_features_scores(
+ scores, knn = self._get_ood_features_scores(
features, **self._get_params(self.OUTLIER_PARAMS)
)
self.params["knn"] = knn
@@ -895,91 +900,105 @@ Source code for cleanlab.outlier
)
else:
self.params["confident_thresholds"] = confident_thresholds
- return scores
+ return scores
+ def _get_ood_features_scores(
+ self,
+ features: Optional[np.ndarray] = None,
+ knn: Optional[NearestNeighbors] = None,
+ k: Optional[int] = None,
+ t: int = 1,
+ ) -> Tuple[np.ndarray, Optional[NearestNeighbors]]:
+ """
+ Return outlier score based on feature values using `k` nearest neighbors.
-def _get_ood_features_scores(
- features: Optional[np.ndarray] = None,
- knn: Optional[NearestNeighbors] = None,
- k: Optional[int] = None,
- t: int = 1,
-) -> Tuple[np.ndarray, Optional[NearestNeighbors]]:
- """
- Return outlier score based on feature values using `k` nearest neighbors.
+ The outlier score for each example is computed inversely proportional to
+ the average distance between this example and its K nearest neighbors (in feature space).
+
+ Parameters
+ ----------
+ features : np.ndarray
+ Feature array of shape ``(N, M)``, where N is the number of examples and M is the number of features used to represent each example.
+ For details, `features` in the same format expected by the `~cleanlab.outlier.OutOfDistribution.fit` function.
- The outlier score for each example is computed inversely proportional to
- the average distance between this example and its K nearest neighbors (in feature space).
+ knn : sklearn.neighbors.NearestNeighbors, default = None
+ For details, see key `knn` in the params dict arg of `~cleanlab.outlier.OutOfDistribution`.
- Parameters
- ----------
- features : np.ndarray
- Feature array of shape ``(N, M)``, where N is the number of examples and M is the number of features used to represent each example.
- For details, `features` in the same format expected by the `~cleanlab.outlier.OutOfDistribution.fit` function.
+ k : int, default=None
+ Optional number of neighbors to use when calculating outlier score (average distance to neighbors).
+ For details, see key `k` in the params dict arg of `~cleanlab.outlier.OutOfDistribution`.
+
+ t : int, default=1
+ Controls transformation of distances between examples into similarity scores that lie in [0,1].
+ For details, see key `t` in the params dict arg of `~cleanlab.outlier.OutOfDistribution`.
+
+ Returns
+ -------
+ ood_features_scores : Tuple[np.ndarray, Optional[NearestNeighbors]]
+ Return a tuple whose first element is array of `ood_features_scores` and second is a `knn` Estimator object.
+ """
+ DEFAULT_K = 10
+ # fit skip over (if knn is not None) then skipping fit and suggest score else fit.
+ if knn is None: # setup default KNN estimator
+ # Make sure both knn and features are not None
+ if features is None:
+ raise ValueError(
+ "Both knn and features arguments cannot be None at the same time. Not enough information to compute outlier scores."
+ )
+ if k is None:
+ k = DEFAULT_K # use default when knn and k are both None
+ if k > len(features): # Ensure number of neighbors less than number of examples
+ raise ValueError(
+ f"Number of nearest neighbors k={k} cannot exceed the number of examples N={len(features)} passed into the estimator (knn)."
+ )
- knn : sklearn.neighbors.NearestNeighbors, default = None
- For details, see key `knn` in the params dict arg of `~cleanlab.outlier.OutOfDistribution`.
+ if features.shape[1] > 3: # use euclidean distance for lower dimensional spaces
+ metric = "cosine"
+ else:
+ metric = "euclidean"
- k : int, default=None
- Optional number of neighbors to use when calculating outlier score (average distance to neighbors).
- For details, see key `k` in the params dict arg of `~cleanlab.outlier.OutOfDistribution`.
+ knn = NearestNeighbors(n_neighbors=k, metric=metric).fit(features)
+ features = None # features should be None in knn.kneighbors(features) to avoid counting duplicate data points
- t : int, default=1
- Controls transformation of distances between examples into similarity scores that lie in [0,1].
- For details, see key `t` in the params dict arg of `~cleanlab.outlier.OutOfDistribution`.
+ elif k is None:
+ k = knn.n_neighbors
- Returns
- -------
- ood_features_scores : Tuple[np.ndarray, Optional[NearestNeighbors]]
- Return a tuple whose first element is array of `ood_features_scores` and second is a `knn` Estimator object.
- """
- DEFAULT_K = 10
- # fit skip over (if knn is not None) then skipping fit and suggest score else fit.
- if knn is None: # setup default KNN estimator
- # Make sure both knn and features are not None
- if features is None:
- raise ValueError(
- "Both knn and features arguments cannot be None at the same time. Not enough information to compute outlier scores."
- )
- if k is None:
- k = DEFAULT_K # use default when knn and k are both None
- if k > len(features): # Ensure number of neighbors less than number of examples
- raise ValueError(
- f"Number of nearest neighbors k={k} cannot exceed the number of examples N={len(features)} passed into the estimator (knn)."
+ max_k = knn.n_neighbors # number of neighbors previously used in NearestNeighbors object
+ if k > max_k: # if k provided is too high, use max possible number of nearest neighbors
+ warnings.warn(
+ f"Chosen k={k} cannot be greater than n_neighbors={max_k} which was used when fitting "
+ f"NearestNeighbors object! Value of k changed to k={max_k}.",
+ UserWarning,
)
+ k = max_k
- if features.shape[1] > 3: # use euclidean distance for lower dimensional spaces
- metric = "cosine"
- else:
- metric = "euclidean"
+ # Fit knn estimator on the features if a non-fitted estimator is passed in
+ try:
+ knn.kneighbors(features)
+ except NotFittedError:
+ knn.fit(features)
- knn = NearestNeighbors(n_neighbors=k, metric=metric).fit(features)
- features = None # features should be None in knn.kneighbors(features) to avoid counting duplicate data points
+ # Get distances to k-nearest neighbors Note that the knn object contains the specification of distance metric
+ # and n_neighbors (k value) If our query set of features matches the training set used to fit knn, the nearest
+ # neighbor of each point is the point itself, at a distance of zero.
+ distances, _ = knn.kneighbors(features)
- elif k is None:
- k = knn.n_neighbors
+ # Calculate average distance to k-nearest neighbors
+ avg_knn_distances = distances[:, :k].mean(axis=1)
- max_k = knn.n_neighbors # number of neighbors previously used in NearestNeighbors object
- if k > max_k: # if k provided is too high, use max possible number of nearest neighbors
- warnings.warn(
- f"Chosen k={k} cannot be greater than n_neighbors={max_k} which was used when fitting "
- f"NearestNeighbors object! Value of k changed to k={max_k}.",
- UserWarning,
- )
- k = max_k
-
- # Fit knn estimator on the features if a non-fitted estimator is passed in
- try:
- knn.kneighbors(features)
- except NotFittedError:
- knn.fit(features)
+ if self.params["scaling_factor"] is None:
+ self.params["scaling_factor"] = float(
+ max(np.median(avg_knn_distances), np.finfo(np.float_).eps)
+ )
+ scaling_factor = self.params["scaling_factor"]
- # Get distances to k-nearest neighbors Note that the knn object contains the specification of distance metric
- # and n_neighbors (k value) If our query set of features matches the training set used to fit knn, the nearest
- # neighbor of each point is the point itself, at a distance of zero.
- distances, _ = knn.kneighbors(features)
+ if not isinstance(scaling_factor, float):
+ raise ValueError(f"Scaling factor must be a float. Got {type(scaling_factor)} instead.")
- ood_features_scores = transform_distances_to_scores(distances, cast(int, k), t)
- return (ood_features_scores, knn)
+ ood_features_scores = transform_distances_to_scores(
+ avg_knn_distances, t, scaling_factor=scaling_factor
+ )
+ return (ood_features_scores, knn)
def _get_ood_predictions_scores(
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index da4542e3a..2eb342976 100644
--- a/master/_sources/tutorials/audio.ipynb
+++ b/master/_sources/tutorials/audio.ipynb
@@ -91,7 +91,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
index bb260a1e0..bd3697463 100644
--- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb
@@ -87,7 +87,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
index 7fbdb32c2..0c3232f18 100644
--- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb
@@ -85,7 +85,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index dbc38e061..989cec61c 100644
--- a/master/_sources/tutorials/datalab/tabular.ipynb
+++ b/master/_sources/tutorials/datalab/tabular.ipynb
@@ -81,7 +81,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb
index d762ff0da..ce28950a0 100644
--- a/master/_sources/tutorials/datalab/text.ipynb
+++ b/master/_sources/tutorials/datalab/text.ipynb
@@ -90,7 +90,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb
index b2513a371..bafc9e946 100644
--- a/master/_sources/tutorials/dataset_health.ipynb
+++ b/master/_sources/tutorials/dataset_health.ipynb
@@ -77,7 +77,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb
index b81792575..eee237852 100644
--- a/master/_sources/tutorials/indepth_overview.ipynb
+++ b/master/_sources/tutorials/indepth_overview.ipynb
@@ -62,7 +62,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb
index b7aca282e..1b283de34 100644
--- a/master/_sources/tutorials/multiannotator.ipynb
+++ b/master/_sources/tutorials/multiannotator.ipynb
@@ -96,7 +96,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb
index 242a117c9..6bdea6099 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -72,7 +72,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb
index 83fdf58e6..ef555678c 100644
--- a/master/_sources/tutorials/object_detection.ipynb
+++ b/master/_sources/tutorials/object_detection.ipynb
@@ -77,7 +77,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb
index 3c063110d..c96e28c2d 100644
--- a/master/_sources/tutorials/outliers.ipynb
+++ b/master/_sources/tutorials/outliers.ipynb
@@ -119,7 +119,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb
index 541d150ac..b3ce7ebb7 100644
--- a/master/_sources/tutorials/regression.ipynb
+++ b/master/_sources/tutorials/regression.ipynb
@@ -103,7 +103,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb
index 7efa15b5d..a311334a3 100644
--- a/master/_sources/tutorials/segmentation.ipynb
+++ b/master/_sources/tutorials/segmentation.ipynb
@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/tabular.ipynb b/master/_sources/tutorials/tabular.ipynb
index f1f1bde5d..43c76fc4f 100644
--- a/master/_sources/tutorials/tabular.ipynb
+++ b/master/_sources/tutorials/tabular.ipynb
@@ -119,7 +119,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/text.ipynb b/master/_sources/tutorials/text.ipynb
index d1a4e7132..6990c53c6 100644
--- a/master/_sources/tutorials/text.ipynb
+++ b/master/_sources/tutorials/text.ipynb
@@ -128,7 +128,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb
index 6227bed63..5d6122eee 100644
--- a/master/_sources/tutorials/token_classification.ipynb
+++ b/master/_sources/tutorials/token_classification.ipynb
@@ -95,7 +95,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/cleanlab/datalab/internal/issue_manager/regression/label.html b/master/cleanlab/datalab/internal/issue_manager/regression/label.html
index d4f3a761d..83e97bddf 100644
--- a/master/cleanlab/datalab/internal/issue_manager/regression/label.html
+++ b/master/cleanlab/datalab/internal/issue_manager/regression/label.html
@@ -561,7 +561,7 @@
-
-class cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager(datalab, clean_learning_kwargs=None, threshold=0.1, health_summary_parameters=None, **_)[source]#
+class cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager(datalab, clean_learning_kwargs=None, threshold=0.05, health_summary_parameters=None, **_)[source]#
Bases: IssueManager
Manages label issues in a Datalab for regression tasks.
@@ -572,7 +572,7 @@
threshold (float) – The threshold to use to determine if an example has a label issue. It is a multiplier
of the median label quality score that sets the absolute threshold. Only used if
predictions are provided to find_issues, not if
-features are provided. Default is 0.1.
diff --git a/master/cleanlab/internal/outlier.html b/master/cleanlab/internal/outlier.html
index 587501cff..d1caf8416 100644
--- a/master/cleanlab/internal/outlier.html
+++ b/master/cleanlab/internal/outlier.html
@@ -541,7 +541,7 @@
How can I find label issues in big datasets with limited memory?
-
+
-
+
@@ -1452,7 +1452,7 @@ Can’t find an answer to your question?new Github issue. Our developers may also provide personalized assistance in our Slack Community.
Professional support and services are also available from our ML experts, learn more by emailing: info@cleanlab.ai
diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb
index 75e655dca..197f0cc47 100644
--- a/master/tutorials/faq.ipynb
+++ b/master/tutorials/faq.ipynb
@@ -18,10 +18,10 @@
"id": "2a4efdde",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:15:57.418817Z",
- "iopub.status.busy": "2024-01-16T18:15:57.418221Z",
- "iopub.status.idle": "2024-01-16T18:15:58.476986Z",
- "shell.execute_reply": "2024-01-16T18:15:58.476369Z"
+ "iopub.execute_input": "2024-01-17T17:47:27.636950Z",
+ "iopub.status.busy": "2024-01-17T17:47:27.636331Z",
+ "iopub.status.idle": "2024-01-17T17:47:28.673494Z",
+ "shell.execute_reply": "2024-01-17T17:47:28.672886Z"
},
"nbsphinx": "hidden"
},
@@ -97,10 +97,10 @@
"id": "239d5ee7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:15:58.480056Z",
- "iopub.status.busy": "2024-01-16T18:15:58.479609Z",
- "iopub.status.idle": "2024-01-16T18:15:58.483407Z",
- "shell.execute_reply": "2024-01-16T18:15:58.482875Z"
+ "iopub.execute_input": "2024-01-17T17:47:28.676967Z",
+ "iopub.status.busy": "2024-01-17T17:47:28.676319Z",
+ "iopub.status.idle": "2024-01-17T17:47:28.680081Z",
+ "shell.execute_reply": "2024-01-17T17:47:28.679568Z"
}
},
"outputs": [],
@@ -136,10 +136,10 @@
"id": "28b324aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:15:58.485825Z",
- "iopub.status.busy": "2024-01-16T18:15:58.485453Z",
- "iopub.status.idle": "2024-01-16T18:16:00.548527Z",
- "shell.execute_reply": "2024-01-16T18:16:00.547841Z"
+ "iopub.execute_input": "2024-01-17T17:47:28.682651Z",
+ "iopub.status.busy": "2024-01-17T17:47:28.682199Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.662113Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.661305Z"
}
},
"outputs": [],
@@ -162,10 +162,10 @@
"id": "28b324ab",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.551920Z",
- "iopub.status.busy": "2024-01-16T18:16:00.551273Z",
- "iopub.status.idle": "2024-01-16T18:16:00.586863Z",
- "shell.execute_reply": "2024-01-16T18:16:00.586069Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.665838Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.664985Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.702802Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.702035Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "90c10e18",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.590183Z",
- "iopub.status.busy": "2024-01-16T18:16:00.589693Z",
- "iopub.status.idle": "2024-01-16T18:16:00.628675Z",
- "shell.execute_reply": "2024-01-16T18:16:00.627875Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.705974Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.705483Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.740910Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.740189Z"
}
},
"outputs": [],
@@ -213,10 +213,10 @@
"id": "88839519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.632522Z",
- "iopub.status.busy": "2024-01-16T18:16:00.632252Z",
- "iopub.status.idle": "2024-01-16T18:16:00.635413Z",
- "shell.execute_reply": "2024-01-16T18:16:00.634891Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.743971Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.743470Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.746772Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.746178Z"
}
},
"outputs": [],
@@ -238,10 +238,10 @@
"id": "558490c2",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.638191Z",
- "iopub.status.busy": "2024-01-16T18:16:00.637827Z",
- "iopub.status.idle": "2024-01-16T18:16:00.640620Z",
- "shell.execute_reply": "2024-01-16T18:16:00.640097Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.749236Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.748768Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.751621Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.751123Z"
}
},
"outputs": [],
@@ -298,10 +298,10 @@
"id": "41714b51",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.643155Z",
- "iopub.status.busy": "2024-01-16T18:16:00.642713Z",
- "iopub.status.idle": "2024-01-16T18:16:00.670851Z",
- "shell.execute_reply": "2024-01-16T18:16:00.670234Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.754142Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.753710Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.781205Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.780598Z"
}
},
"outputs": [
@@ -315,7 +315,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "be6b89c05bf54e629d5214f135ecc2d9",
+ "model_id": "6118415ae7394ffd96f150429a30c90e",
"version_major": 2,
"version_minor": 0
},
@@ -329,7 +329,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "55e901a991824e3fbda53e2d393455d9",
+ "model_id": "74b94b74c8204c518111c5334e52842b",
"version_major": 2,
"version_minor": 0
},
@@ -387,10 +387,10 @@
"id": "20476c70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.678225Z",
- "iopub.status.busy": "2024-01-16T18:16:00.677825Z",
- "iopub.status.idle": "2024-01-16T18:16:00.684491Z",
- "shell.execute_reply": "2024-01-16T18:16:00.683970Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.788205Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.787789Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.794524Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.794019Z"
},
"nbsphinx": "hidden"
},
@@ -421,10 +421,10 @@
"id": "6983cdad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.686812Z",
- "iopub.status.busy": "2024-01-16T18:16:00.686368Z",
- "iopub.status.idle": "2024-01-16T18:16:00.690083Z",
- "shell.execute_reply": "2024-01-16T18:16:00.689559Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.796854Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.796459Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.800312Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.799775Z"
},
"nbsphinx": "hidden"
},
@@ -447,10 +447,10 @@
"id": "9092b8a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.692161Z",
- "iopub.status.busy": "2024-01-16T18:16:00.691959Z",
- "iopub.status.idle": "2024-01-16T18:16:00.699054Z",
- "shell.execute_reply": "2024-01-16T18:16:00.698536Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.802703Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.802357Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.809258Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.808727Z"
}
},
"outputs": [],
@@ -500,10 +500,10 @@
"id": "b0a01109",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.701373Z",
- "iopub.status.busy": "2024-01-16T18:16:00.700937Z",
- "iopub.status.idle": "2024-01-16T18:16:00.739221Z",
- "shell.execute_reply": "2024-01-16T18:16:00.738543Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.811612Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.811247Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.848892Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.848075Z"
}
},
"outputs": [],
@@ -520,10 +520,10 @@
"id": "8b1da032",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.742109Z",
- "iopub.status.busy": "2024-01-16T18:16:00.741846Z",
- "iopub.status.idle": "2024-01-16T18:16:00.780149Z",
- "shell.execute_reply": "2024-01-16T18:16:00.779344Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.852080Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.851683Z",
+ "iopub.status.idle": "2024-01-17T17:47:30.890113Z",
+ "shell.execute_reply": "2024-01-17T17:47:30.889434Z"
},
"nbsphinx": "hidden"
},
@@ -602,10 +602,10 @@
"id": "4c9e9030",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.783325Z",
- "iopub.status.busy": "2024-01-16T18:16:00.783062Z",
- "iopub.status.idle": "2024-01-16T18:16:00.902882Z",
- "shell.execute_reply": "2024-01-16T18:16:00.902217Z"
+ "iopub.execute_input": "2024-01-17T17:47:30.893308Z",
+ "iopub.status.busy": "2024-01-17T17:47:30.892889Z",
+ "iopub.status.idle": "2024-01-17T17:47:31.012408Z",
+ "shell.execute_reply": "2024-01-17T17:47:31.011727Z"
}
},
"outputs": [
@@ -672,10 +672,10 @@
"id": "8751619e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:00.905753Z",
- "iopub.status.busy": "2024-01-16T18:16:00.905355Z",
- "iopub.status.idle": "2024-01-16T18:16:03.441187Z",
- "shell.execute_reply": "2024-01-16T18:16:03.440427Z"
+ "iopub.execute_input": "2024-01-17T17:47:31.015249Z",
+ "iopub.status.busy": "2024-01-17T17:47:31.014843Z",
+ "iopub.status.idle": "2024-01-17T17:47:33.505335Z",
+ "shell.execute_reply": "2024-01-17T17:47:33.504572Z"
}
},
"outputs": [
@@ -761,10 +761,10 @@
"id": "623df36d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:03.444116Z",
- "iopub.status.busy": "2024-01-16T18:16:03.443692Z",
- "iopub.status.idle": "2024-01-16T18:16:03.502832Z",
- "shell.execute_reply": "2024-01-16T18:16:03.502159Z"
+ "iopub.execute_input": "2024-01-17T17:47:33.508187Z",
+ "iopub.status.busy": "2024-01-17T17:47:33.507786Z",
+ "iopub.status.idle": "2024-01-17T17:47:33.565362Z",
+ "shell.execute_reply": "2024-01-17T17:47:33.564757Z"
}
},
"outputs": [
@@ -802,7 +802,7 @@
},
{
"cell_type": "markdown",
- "id": "a63586b5",
+ "id": "2e2ede4c",
"metadata": {},
"source": [
"### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?"
@@ -810,7 +810,7 @@
},
{
"cell_type": "markdown",
- "id": "2b31716f",
+ "id": "b85b170d",
"metadata": {},
"source": [
"When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n",
@@ -823,13 +823,13 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "4887263e",
+ "id": "f888ecd3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:03.505582Z",
- "iopub.status.busy": "2024-01-16T18:16:03.505191Z",
- "iopub.status.idle": "2024-01-16T18:16:03.613248Z",
- "shell.execute_reply": "2024-01-16T18:16:03.612541Z"
+ "iopub.execute_input": "2024-01-17T17:47:33.567937Z",
+ "iopub.status.busy": "2024-01-17T17:47:33.567572Z",
+ "iopub.status.idle": "2024-01-17T17:47:33.681531Z",
+ "shell.execute_reply": "2024-01-17T17:47:33.680872Z"
}
},
"outputs": [
@@ -870,7 +870,7 @@
},
{
"cell_type": "markdown",
- "id": "699098e7",
+ "id": "035f5521",
"metadata": {},
"source": [
"For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:"
@@ -879,13 +879,13 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "f8a445ea",
+ "id": "4eca8e3f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:03.616923Z",
- "iopub.status.busy": "2024-01-16T18:16:03.616277Z",
- "iopub.status.idle": "2024-01-16T18:16:03.693439Z",
- "shell.execute_reply": "2024-01-16T18:16:03.692848Z"
+ "iopub.execute_input": "2024-01-17T17:47:33.684745Z",
+ "iopub.status.busy": "2024-01-17T17:47:33.684425Z",
+ "iopub.status.idle": "2024-01-17T17:47:33.755644Z",
+ "shell.execute_reply": "2024-01-17T17:47:33.754828Z"
}
},
"outputs": [
@@ -921,7 +921,7 @@
},
{
"cell_type": "markdown",
- "id": "6af9e5a8",
+ "id": "02bcd7ad",
"metadata": {},
"source": [
"### How to handle near-duplicate data identified by cleanlab?\n",
@@ -932,13 +932,13 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "326f5f2c",
+ "id": "8d96a258",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:03.696323Z",
- "iopub.status.busy": "2024-01-16T18:16:03.695897Z",
- "iopub.status.idle": "2024-01-16T18:16:03.704431Z",
- "shell.execute_reply": "2024-01-16T18:16:03.703903Z"
+ "iopub.execute_input": "2024-01-17T17:47:33.758921Z",
+ "iopub.status.busy": "2024-01-17T17:47:33.758478Z",
+ "iopub.status.idle": "2024-01-17T17:47:33.766911Z",
+ "shell.execute_reply": "2024-01-17T17:47:33.766290Z"
}
},
"outputs": [],
@@ -1040,7 +1040,7 @@
},
{
"cell_type": "markdown",
- "id": "ac16d60c",
+ "id": "226cb25c",
"metadata": {},
"source": [
"The functions above collect sets of near-duplicate examples. Within each\n",
@@ -1055,13 +1055,13 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "38316429",
+ "id": "7d3ac0da",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-16T18:16:03.706739Z",
- "iopub.status.busy": "2024-01-16T18:16:03.706553Z",
- "iopub.status.idle": "2024-01-16T18:16:03.725715Z",
- "shell.execute_reply": "2024-01-16T18:16:03.725160Z"
+ "iopub.execute_input": "2024-01-17T17:47:33.769300Z",
+ "iopub.status.busy": "2024-01-17T17:47:33.768858Z",
+ "iopub.status.idle": "2024-01-17T17:47:33.788533Z",
+ "shell.execute_reply": "2024-01-17T17:47:33.787972Z"
}
},
"outputs": [
@@ -1104,13 +1104,13 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "4475f99d",
+ "id": "5f110e92",
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diff --git a/master/tutorials/image.html b/master/tutorials/image.html
index 39733fb1f..6e01469cf 100644
--- a/master/tutorials/image.html
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@@ -887,25 +887,25 @@ 2. Fetch and normalize the Fashion-MNIST dataset
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Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.
-epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.688
+epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.530
-epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.468
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-
+Finished Training
-Finished Training
+
Reorder rows of the dataset based on row order in features and pred_probs. Carefully ensure your ordering of the dataset matches these objects!
As before, these label quality scores are continuous values in the range [0,1] where 1 represents a clean label (given label appears correct) and 0 a represents dirty label (given label appears corrupted). You can sort examples by their label quality scores to inspect the most-likely corrupted datapoints.
diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb index 6625c8123..29c95d1fd 100644 --- a/master/tutorials/regression.ipynb +++ b/master/tutorials/regression.ipynb @@ -94,10 +94,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:38.116838Z", - "iopub.status.busy": "2024-01-16T18:22:38.116637Z", - "iopub.status.idle": "2024-01-16T18:22:39.187452Z", - "shell.execute_reply": "2024-01-16T18:22:39.186824Z" + "iopub.execute_input": "2024-01-17T17:54:15.007278Z", + "iopub.status.busy": "2024-01-17T17:54:15.007085Z", + "iopub.status.idle": "2024-01-17T17:54:16.086532Z", + "shell.execute_reply": "2024-01-17T17:54:16.085857Z" }, "nbsphinx": "hidden" }, @@ -109,7 +109,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:39.190525Z", - "iopub.status.busy": "2024-01-16T18:22:39.190058Z", - "iopub.status.idle": "2024-01-16T18:22:39.205860Z", - "shell.execute_reply": "2024-01-16T18:22:39.205379Z" + "iopub.execute_input": "2024-01-17T17:54:16.089484Z", + "iopub.status.busy": "2024-01-17T17:54:16.089186Z", + "iopub.status.idle": "2024-01-17T17:54:16.105268Z", + "shell.execute_reply": "2024-01-17T17:54:16.104669Z" } }, "outputs": [], @@ -157,10 +157,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:39.208336Z", - "iopub.status.busy": "2024-01-16T18:22:39.207956Z", - "iopub.status.idle": "2024-01-16T18:22:39.211011Z", - "shell.execute_reply": "2024-01-16T18:22:39.210464Z" + "iopub.execute_input": "2024-01-17T17:54:16.107803Z", + "iopub.status.busy": "2024-01-17T17:54:16.107440Z", + "iopub.status.idle": "2024-01-17T17:54:16.110701Z", + "shell.execute_reply": "2024-01-17T17:54:16.110172Z" }, "nbsphinx": "hidden" }, @@ -191,10 +191,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:39.213387Z", - "iopub.status.busy": "2024-01-16T18:22:39.213031Z", - "iopub.status.idle": "2024-01-16T18:22:39.320048Z", - "shell.execute_reply": "2024-01-16T18:22:39.319407Z" + "iopub.execute_input": "2024-01-17T17:54:16.112895Z", + "iopub.status.busy": "2024-01-17T17:54:16.112701Z", + "iopub.status.idle": "2024-01-17T17:54:16.421711Z", + "shell.execute_reply": "2024-01-17T17:54:16.421116Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:39.322833Z", - "iopub.status.busy": "2024-01-16T18:22:39.322456Z", - "iopub.status.idle": "2024-01-16T18:22:39.591964Z", - "shell.execute_reply": "2024-01-16T18:22:39.591358Z" + "iopub.execute_input": "2024-01-17T17:54:16.424402Z", + "iopub.status.busy": "2024-01-17T17:54:16.423998Z", + "iopub.status.idle": "2024-01-17T17:54:16.692752Z", + "shell.execute_reply": "2024-01-17T17:54:16.692024Z" }, "nbsphinx": "hidden" }, @@ -410,10 +410,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:39.594806Z", - "iopub.status.busy": "2024-01-16T18:22:39.594399Z", - "iopub.status.idle": "2024-01-16T18:22:39.848239Z", - "shell.execute_reply": "2024-01-16T18:22:39.847546Z" + "iopub.execute_input": "2024-01-17T17:54:16.695418Z", + "iopub.status.busy": "2024-01-17T17:54:16.695200Z", + "iopub.status.idle": "2024-01-17T17:54:16.949304Z", + "shell.execute_reply": "2024-01-17T17:54:16.948645Z" } }, "outputs": [ @@ -449,10 +449,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:39.851127Z", - "iopub.status.busy": "2024-01-16T18:22:39.850474Z", - "iopub.status.idle": "2024-01-16T18:22:39.855174Z", - "shell.execute_reply": "2024-01-16T18:22:39.854672Z" + "iopub.execute_input": "2024-01-17T17:54:16.951844Z", + "iopub.status.busy": "2024-01-17T17:54:16.951634Z", + "iopub.status.idle": "2024-01-17T17:54:16.956470Z", + "shell.execute_reply": "2024-01-17T17:54:16.955953Z" } }, "outputs": [], @@ -470,10 +470,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:39.857546Z", - "iopub.status.busy": "2024-01-16T18:22:39.857120Z", - "iopub.status.idle": "2024-01-16T18:22:39.863760Z", - "shell.execute_reply": "2024-01-16T18:22:39.863275Z" + "iopub.execute_input": "2024-01-17T17:54:16.958787Z", + "iopub.status.busy": "2024-01-17T17:54:16.958432Z", + "iopub.status.idle": "2024-01-17T17:54:16.964248Z", + "shell.execute_reply": "2024-01-17T17:54:16.963768Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:39.866099Z", - "iopub.status.busy": "2024-01-16T18:22:39.865901Z", - "iopub.status.idle": "2024-01-16T18:22:39.868607Z", - "shell.execute_reply": "2024-01-16T18:22:39.868059Z" + "iopub.execute_input": "2024-01-17T17:54:16.966530Z", + "iopub.status.busy": "2024-01-17T17:54:16.966188Z", + "iopub.status.idle": "2024-01-17T17:54:16.969045Z", + "shell.execute_reply": "2024-01-17T17:54:16.968434Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:39.870939Z", - "iopub.status.busy": "2024-01-16T18:22:39.870573Z", - "iopub.status.idle": "2024-01-16T18:22:50.023137Z", - "shell.execute_reply": "2024-01-16T18:22:50.022407Z" + "iopub.execute_input": "2024-01-17T17:54:16.971259Z", + "iopub.status.busy": "2024-01-17T17:54:16.970905Z", + "iopub.status.idle": "2024-01-17T17:54:27.352992Z", + "shell.execute_reply": "2024-01-17T17:54:27.352338Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:50.026675Z", - "iopub.status.busy": "2024-01-16T18:22:50.026017Z", - "iopub.status.idle": "2024-01-16T18:22:50.033605Z", - "shell.execute_reply": "2024-01-16T18:22:50.032991Z" + "iopub.execute_input": "2024-01-17T17:54:27.356637Z", + "iopub.status.busy": "2024-01-17T17:54:27.355930Z", + "iopub.status.idle": "2024-01-17T17:54:27.363535Z", + "shell.execute_reply": "2024-01-17T17:54:27.362912Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:50.036105Z", - "iopub.status.busy": "2024-01-16T18:22:50.035725Z", - "iopub.status.idle": "2024-01-16T18:22:50.039438Z", - "shell.execute_reply": "2024-01-16T18:22:50.038945Z" + "iopub.execute_input": "2024-01-17T17:54:27.366166Z", + "iopub.status.busy": "2024-01-17T17:54:27.365789Z", + "iopub.status.idle": "2024-01-17T17:54:27.369511Z", + "shell.execute_reply": "2024-01-17T17:54:27.369017Z" } }, "outputs": [], @@ -689,10 +689,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:50.041787Z", - "iopub.status.busy": "2024-01-16T18:22:50.041427Z", - "iopub.status.idle": "2024-01-16T18:22:50.044834Z", - "shell.execute_reply": "2024-01-16T18:22:50.044228Z" + "iopub.execute_input": "2024-01-17T17:54:27.371734Z", + "iopub.status.busy": "2024-01-17T17:54:27.371389Z", + "iopub.status.idle": "2024-01-17T17:54:27.375010Z", + "shell.execute_reply": "2024-01-17T17:54:27.374392Z" } }, "outputs": [ @@ -727,10 +727,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:50.047247Z", - "iopub.status.busy": "2024-01-16T18:22:50.046886Z", - "iopub.status.idle": "2024-01-16T18:22:50.050075Z", - "shell.execute_reply": "2024-01-16T18:22:50.049525Z" + "iopub.execute_input": "2024-01-17T17:54:27.377324Z", + "iopub.status.busy": "2024-01-17T17:54:27.376975Z", + "iopub.status.idle": "2024-01-17T17:54:27.380277Z", + "shell.execute_reply": "2024-01-17T17:54:27.379739Z" } }, "outputs": [], @@ -749,10 +749,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:22:50.052062Z", - "iopub.status.busy": "2024-01-16T18:22:50.051866Z", - "iopub.status.idle": "2024-01-16T18:22:50.060351Z", - "shell.execute_reply": "2024-01-16T18:22:50.059773Z" + "iopub.execute_input": "2024-01-17T17:54:27.382482Z", + "iopub.status.busy": "2024-01-17T17:54:27.382140Z", + "iopub.status.idle": "2024-01-17T17:54:27.390755Z", + "shell.execute_reply": "2024-01-17T17:54:27.390135Z" } }, "outputs": [ @@ -894,10 +894,10 @@ "id": "31c704e7", "metadata": { "execution": { - 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+- 6%|▋ | 312395/4997817 [00:01<00:26, 174906.22it/s]
- -
</pre>
- 6%|▋ | 316949/4997817 [00:01<00:26, 174831.53it/s]
- 6%|▋ | 312395/4997817 [00:01<00:26, 174906.22it/s]
end{sphinxVerbatim}
6%|▋ | 316949/4997817 [00:01<00:26, 174831.53it/s]
+6%|▋ | 312395/4997817 [00:01<00:26, 174906.22it/s]
+- 7%|▋ | 329935/4997817 [00:01<00:26, 175050.91it/s]
- -
</pre>
- 7%|▋ | 334447/4997817 [00:01<00:26, 174672.59it/s]
- 7%|▋ | 329935/4997817 [00:01<00:26, 175050.91it/s]
end{sphinxVerbatim}
7%|▋ | 334447/4997817 [00:01<00:26, 174672.59it/s]
+7%|▋ | 329935/4997817 [00:01<00:26, 175050.91it/s]
+- 7%|▋ | 347443/4997817 [00:02<00:26, 175054.81it/s]
- -
</pre>
- 7%|▋ | 352070/4997817 [00:02<00:26, 175096.63it/s]
- 7%|▋ | 347443/4997817 [00:02<00:26, 175054.81it/s]
end{sphinxVerbatim}
7%|▋ | 352070/4997817 [00:02<00:26, 175096.63it/s]
+7%|▋ | 347443/4997817 [00:02<00:26, 175054.81it/s]
+- 7%|▋ | 364973/4997817 [00:02<00:26, 175126.29it/s]
- -
</pre>
- 7%|▋ | 369727/4997817 [00:02<00:26, 175534.32it/s]
- 7%|▋ | 364973/4997817 [00:02<00:26, 175126.29it/s]
end{sphinxVerbatim}
7%|▋ | 369727/4997817 [00:02<00:26, 175534.32it/s]
+7%|▋ | 364973/4997817 [00:02<00:26, 175126.29it/s]
+- 8%|▊ | 382494/4997817 [00:02<00:26, 175147.44it/s]
- -
</pre>
- 8%|▊ | 387433/4997817 [00:02<00:26, 175988.24it/s]
- 8%|▊ | 382494/4997817 [00:02<00:26, 175147.44it/s]
end{sphinxVerbatim}
8%|▊ | 387433/4997817 [00:02<00:26, 175988.24it/s]
+8%|▊ | 382494/4997817 [00:02<00:26, 175147.44it/s]
+- 8%|▊ | 400041/4997817 [00:02<00:26, 175240.87it/s]
- -
</pre>
- 8%|▊ | 405082/4997817 [00:02<00:26, 176134.84it/s]
- 8%|▊ | 400041/4997817 [00:02<00:26, 175240.87it/s]
end{sphinxVerbatim}
8%|▊ | 405082/4997817 [00:02<00:26, 176134.84it/s]
+8%|▊ | 400041/4997817 [00:02<00:26, 175240.87it/s]
+- 8%|▊ | 417566/4997817 [00:02<00:26, 173702.60it/s]
- -
</pre>
- 8%|▊ | 422783/4997817 [00:02<00:25, 176395.51it/s]
- 8%|▊ | 417566/4997817 [00:02<00:26, 173702.60it/s]
end{sphinxVerbatim}
8%|▊ | 422783/4997817 [00:02<00:25, 176395.51it/s]
+8%|▊ | 417566/4997817 [00:02<00:26, 173702.60it/s]
+- 9%|▊ | 435051/4997817 [00:02<00:26, 174043.25it/s]
- -
</pre>
- 9%|▉ | 440425/4997817 [00:02<00:25, 176241.72it/s]
- 9%|▊ | 435051/4997817 [00:02<00:26, 174043.25it/s]
end{sphinxVerbatim}
9%|▉ | 440425/4997817 [00:02<00:25, 176241.72it/s]
+9%|▊ | 435051/4997817 [00:02<00:26, 174043.25it/s]
+- 9%|▉ | 452540/4997817 [00:02<00:26, 174293.87it/s]
- -
</pre>
- 9%|▉ | 458051/4997817 [00:02<00:25, 175933.35it/s]
- 9%|▉ | 452540/4997817 [00:02<00:26, 174293.87it/s]
end{sphinxVerbatim}
9%|▉ | 458051/4997817 [00:02<00:25, 175933.35it/s]
+9%|▉ | 452540/4997817 [00:02<00:26, 174293.87it/s]
+- 9%|▉ | 470079/4997817 [00:02<00:25, 174620.38it/s]
- -
</pre>
- 10%|▉ | 475646/4997817 [00:02<00:25, 175259.61it/s]
- 9%|▉ | 470079/4997817 [00:02<00:25, 174620.38it/s]
end{sphinxVerbatim}
10%|▉ | 475646/4997817 [00:02<00:25, 175259.61it/s]
+9%|▉ | 470079/4997817 [00:02<00:25, 174620.38it/s]
+- 10%|▉ | 487543/4997817 [00:02<00:25, 174621.94it/s]
- -
</pre>
- 10%|▉ | 493283/4997817 [00:02<00:25, 175589.10it/s]
- 10%|▉ | 487543/4997817 [00:02<00:25, 174621.94it/s]
end{sphinxVerbatim}
10%|▉ | 493283/4997817 [00:02<00:25, 175589.10it/s]
+10%|▉ | 487543/4997817 [00:02<00:25, 174621.94it/s]
+- 10%|█ | 505007/4997817 [00:02<00:25, 174578.03it/s]
- -
</pre>
- 10%|█ | 510968/4997817 [00:02<00:25, 175964.15it/s]
- 10%|█ | 505007/4997817 [00:02<00:25, 174578.03it/s]
end{sphinxVerbatim}
10%|█ | 510968/4997817 [00:02<00:25, 175964.15it/s]
+10%|█ | 505007/4997817 [00:02<00:25, 174578.03it/s]
+- 10%|█ | 522466/4997817 [00:03<00:26, 167350.37it/s]
- -
</pre>
- 11%|█ | 528791/4997817 [00:03<00:25, 176639.75it/s]
- 10%|█ | 522466/4997817 [00:03<00:26, 167350.37it/s]
end{sphinxVerbatim}
11%|█ | 528791/4997817 [00:03<00:25, 176639.75it/s]
+10%|█ | 522466/4997817 [00:03<00:26, 167350.37it/s]
+- 11%|█ | 539898/4997817 [00:03<00:26, 169376.69it/s]
- -
</pre>
- 11%|█ | 546506/4997817 [00:03<00:25, 176790.75it/s]
- 11%|█ | 539898/4997817 [00:03<00:26, 169376.69it/s]
end{sphinxVerbatim}
11%|█ | 546506/4997817 [00:03<00:25, 176790.75it/s]
+11%|█ | 539898/4997817 [00:03<00:26, 169376.69it/s]
+- 11%|█ | 557335/4997817 [00:03<00:25, 170838.31it/s]
- -
</pre>
- 11%|█▏ | 564262/4997817 [00:03<00:25, 177017.54it/s]
- 11%|█ | 557335/4997817 [00:03<00:25, 170838.31it/s]
end{sphinxVerbatim}
11%|█▏ | 564262/4997817 [00:03<00:25, 177017.54it/s]
+11%|█ | 557335/4997817 [00:03<00:25, 170838.31it/s]
+- 11%|█▏ | 574736/4997817 [00:03<00:25, 171773.60it/s]
- -
</pre>
- 12%|█▏ | 581997/4997817 [00:03<00:24, 177113.84it/s]
- 11%|█▏ | 574736/4997817 [00:03<00:25, 171773.60it/s]
end{sphinxVerbatim}
12%|█▏ | 581997/4997817 [00:03<00:24, 177113.84it/s]
+11%|█▏ | 574736/4997817 [00:03<00:25, 171773.60it/s]
+- 12%|█▏ | 592170/4997817 [00:03<00:25, 172531.38it/s]
- -
</pre>
- 12%|█▏ | 599709/4997817 [00:03<00:24, 176690.32it/s]
- 12%|█▏ | 592170/4997817 [00:03<00:25, 172531.38it/s]
end{sphinxVerbatim}
12%|█▏ | 599709/4997817 [00:03<00:24, 176690.32it/s]
+12%|█▏ | 592170/4997817 [00:03<00:25, 172531.38it/s]
+- 12%|█▏ | 609571/4997817 [00:03<00:25, 172968.76it/s]
- -
</pre>
- 12%|█▏ | 617412/4997817 [00:03<00:24, 176780.08it/s]
- 12%|█▏ | 609571/4997817 [00:03<00:25, 172968.76it/s]
end{sphinxVerbatim}
12%|█▏ | 617412/4997817 [00:03<00:24, 176780.08it/s]
+12%|█▏ | 609571/4997817 [00:03<00:25, 172968.76it/s]
+- 13%|█▎ | 627002/4997817 [00:03<00:25, 173366.60it/s]
- -
</pre>
- 13%|█▎ | 635091/4997817 [00:03<00:24, 176406.02it/s]
- 13%|█▎ | 627002/4997817 [00:03<00:25, 173366.60it/s]
end{sphinxVerbatim}
13%|█▎ | 635091/4997817 [00:03<00:24, 176406.02it/s]
+13%|█▎ | 627002/4997817 [00:03<00:25, 173366.60it/s]
+- 13%|█▎ | 644443/4997817 [00:03<00:25, 173677.03it/s]
- -
</pre>
- 13%|█▎ | 652732/4997817 [00:03<00:24, 176237.39it/s]
- 13%|█▎ | 644443/4997817 [00:03<00:25, 173677.03it/s]
end{sphinxVerbatim}
13%|█▎ | 652732/4997817 [00:03<00:24, 176237.39it/s]
+13%|█▎ | 644443/4997817 [00:03<00:25, 173677.03it/s]
+- 13%|█▎ | 661854/4997817 [00:03<00:24, 173804.40it/s]
- -
</pre>
- 13%|█▎ | 670432/4997817 [00:03<00:24, 176462.89it/s]
- 13%|█▎ | 661854/4997817 [00:03<00:24, 173804.40it/s]
end{sphinxVerbatim}
13%|█▎ | 670432/4997817 [00:03<00:24, 176462.89it/s]
+13%|█▎ | 661854/4997817 [00:03<00:24, 173804.40it/s]
+- 14%|█▎ | 679240/4997817 [00:03<00:24, 173608.41it/s]
- -
</pre>
- 14%|█▍ | 688079/4997817 [00:03<00:24, 176157.92it/s]
- 14%|█▎ | 679240/4997817 [00:03<00:24, 173608.41it/s]
end{sphinxVerbatim}
14%|█▍ | 688079/4997817 [00:03<00:24, 176157.92it/s]
+14%|█▎ | 679240/4997817 [00:03<00:24, 173608.41it/s]
+- 14%|█▍ | 696605/4997817 [00:04<00:24, 173292.90it/s]
- -
</pre>
- 14%|█▍ | 705696/4997817 [00:04<00:24, 175449.15it/s]
- 14%|█▍ | 696605/4997817 [00:04<00:24, 173292.90it/s]
end{sphinxVerbatim}
14%|█▍ | 705696/4997817 [00:04<00:24, 175449.15it/s]
+14%|█▍ | 696605/4997817 [00:04<00:24, 173292.90it/s]
+- 14%|█▍ | 713937/4997817 [00:04<00:24, 173104.99it/s]
- -
</pre>
- 14%|█▍ | 723242/4997817 [00:04<00:24, 174539.39it/s]
- 14%|█▍ | 713937/4997817 [00:04<00:24, 173104.99it/s]
end{sphinxVerbatim}
14%|█▍ | 723242/4997817 [00:04<00:24, 174539.39it/s]
+14%|█▍ | 713937/4997817 [00:04<00:24, 173104.99it/s]
+- 15%|█▍ | 731313/4997817 [00:04<00:24, 173297.79it/s]
- -
</pre>
- 15%|█▍ | 740698/4997817 [00:04<00:24, 173584.50it/s]
- 15%|█▍ | 731313/4997817 [00:04<00:24, 173297.79it/s]
end{sphinxVerbatim}
15%|█▍ | 740698/4997817 [00:04<00:24, 173584.50it/s]
+15%|█▍ | 731313/4997817 [00:04<00:24, 173297.79it/s]
+- 15%|█▍ | 748645/4997817 [00:04<00:24, 173293.85it/s]
- -
</pre>
- 15%|█▌ | 758058/4997817 [00:04<00:24, 172795.80it/s]
- 15%|█▍ | 748645/4997817 [00:04<00:24, 173293.85it/s]
end{sphinxVerbatim}
15%|█▌ | 758058/4997817 [00:04<00:24, 172795.80it/s]
+15%|█▍ | 748645/4997817 [00:04<00:24, 173293.85it/s]
+- 15%|█▌ | 765976/4997817 [00:04<00:24, 173226.36it/s]
- -
</pre>
- 16%|█▌ | 775339/4997817 [00:04<00:24, 172581.89it/s]
- 15%|█▌ | 765976/4997817 [00:04<00:24, 173226.36it/s]
end{sphinxVerbatim}
16%|█▌ | 775339/4997817 [00:04<00:24, 172581.89it/s]
+15%|█▌ | 765976/4997817 [00:04<00:24, 173226.36it/s]
+- 16%|█▌ | 783313/4997817 [00:04<00:24, 173268.21it/s]
- -
</pre>
- 16%|█▌ | 792598/4997817 [00:04<00:24, 172165.33it/s]
- 16%|█▌ | 783313/4997817 [00:04<00:24, 173268.21it/s]
end{sphinxVerbatim}
16%|█▌ | 792598/4997817 [00:04<00:24, 172165.33it/s]
+16%|█▌ | 783313/4997817 [00:04<00:24, 173268.21it/s]
+- 16%|█▌ | 800713/4997817 [00:04<00:24, 173485.98it/s]
- -
</pre>
- 16%|█▌ | 809815/4997817 [00:04<00:25, 167297.60it/s]
- 16%|█▌ | 800713/4997817 [00:04<00:24, 173485.98it/s]
end{sphinxVerbatim}
16%|█▌ | 809815/4997817 [00:04<00:25, 167297.60it/s]
+16%|█▌ | 800713/4997817 [00:04<00:24, 173485.98it/s]
+- 16%|█▋ | 818145/4997817 [00:04<00:24, 173734.84it/s]
- -
</pre>
- 17%|█▋ | 826573/4997817 [00:04<00:24, 167371.05it/s]
- 16%|█▋ | 818145/4997817 [00:04<00:24, 173734.84it/s]
end{sphinxVerbatim}
17%|█▋ | 826573/4997817 [00:04<00:24, 167371.05it/s]
+16%|█▋ | 818145/4997817 [00:04<00:24, 173734.84it/s]
+- 17%|█▋ | 835547/4997817 [00:04<00:23, 173817.17it/s]
- -
</pre>
- 17%|█▋ | 843744/4997817 [00:04<00:24, 168646.95it/s]
- 17%|█▋ | 835547/4997817 [00:04<00:23, 173817.17it/s]
end{sphinxVerbatim}
17%|█▋ | 843744/4997817 [00:04<00:24, 168646.95it/s]
+17%|█▋ | 835547/4997817 [00:04<00:23, 173817.17it/s]
+- 17%|█▋ | 853044/4997817 [00:04<00:23, 174158.84it/s]
- -
</pre>
- 17%|█▋ | 860832/4997817 [00:04<00:24, 169304.60it/s]
- 17%|█▋ | 853044/4997817 [00:04<00:23, 174158.84it/s]
end{sphinxVerbatim}
17%|█▋ | 860832/4997817 [00:04<00:24, 169304.60it/s]
+17%|█▋ | 853044/4997817 [00:04<00:23, 174158.84it/s]
+- 17%|█▋ | 870461/4997817 [00:05<00:24, 169906.69it/s]
- -
</pre>
- 18%|█▊ | 877949/4997817 [00:05<00:24, 169856.65it/s]
- 17%|█▋ | 870461/4997817 [00:05<00:24, 169906.69it/s]
end{sphinxVerbatim}
18%|█▊ | 877949/4997817 [00:05<00:24, 169856.65it/s]
+17%|█▋ | 870461/4997817 [00:05<00:24, 169906.69it/s]
+- 18%|█▊ | 887788/4997817 [00:05<00:24, 170896.25it/s]
- -
</pre>
- 18%|█▊ | 895277/4997817 [00:05<00:24, 170874.65it/s]
- 18%|█▊ | 887788/4997817 [00:05<00:24, 170896.25it/s]
end{sphinxVerbatim}
18%|█▊ | 895277/4997817 [00:05<00:24, 170874.65it/s]
+18%|█▊ | 887788/4997817 [00:05<00:24, 170896.25it/s]
+- 18%|█▊ | 905410/4997817 [00:05<00:23, 172468.13it/s]
- -
</pre>
- 18%|█▊ | 912949/4997817 [00:05<00:23, 172617.35it/s]
- 18%|█▊ | 905410/4997817 [00:05<00:23, 172468.13it/s]
end{sphinxVerbatim}
18%|█▊ | 912949/4997817 [00:05<00:23, 172617.35it/s]
+18%|█▊ | 905410/4997817 [00:05<00:23, 172468.13it/s]
+- 18%|█▊ | 922998/4997817 [00:05<00:23, 173479.95it/s]
- -
</pre>
- 19%|█▊ | 930539/4997817 [00:05<00:23, 173597.86it/s]
- 18%|█▊ | 922998/4997817 [00:05<00:23, 173479.95it/s]
end{sphinxVerbatim}
19%|█▊ | 930539/4997817 [00:05<00:23, 173597.86it/s]
+18%|█▊ | 922998/4997817 [00:05<00:23, 173479.95it/s]
+- 19%|█▉ | 940562/4997817 [00:05<00:23, 174120.12it/s]
- -
</pre>
- 19%|█▉ | 948067/4997817 [00:05<00:23, 174098.75it/s]
- 19%|█▉ | 940562/4997817 [00:05<00:23, 174120.12it/s]
end{sphinxVerbatim}
19%|█▉ | 948067/4997817 [00:05<00:23, 174098.75it/s]
+19%|█▉ | 940562/4997817 [00:05<00:23, 174120.12it/s]
+- 19%|█▉ | 958279/4997817 [00:05<00:23, 175029.36it/s]
- -
</pre>
- 19%|█▉ | 965686/4997817 [00:05<00:23, 174723.74it/s]
- 19%|█▉ | 958279/4997817 [00:05<00:23, 175029.36it/s]
end{sphinxVerbatim}
19%|█▉ | 965686/4997817 [00:05<00:23, 174723.74it/s]
+19%|█▉ | 958279/4997817 [00:05<00:23, 175029.36it/s]
+- 20%|█▉ | 976026/4997817 [00:05<00:22, 175755.39it/s]
- -
</pre>
- 20%|█▉ | 983278/4997817 [00:05<00:22, 175080.04it/s]
- 20%|█▉ | 976026/4997817 [00:05<00:22, 175755.39it/s]
end{sphinxVerbatim}
20%|█▉ | 983278/4997817 [00:05<00:22, 175080.04it/s]
+20%|█▉ | 976026/4997817 [00:05<00:22, 175755.39it/s]
+- 20%|█▉ | 993657/4997817 [00:05<00:22, 175917.83it/s]
- -
</pre>
- 20%|██ | 1000897/4997817 [00:05<00:22, 175408.44it/s]
- 20%|█▉ | 993657/4997817 [00:05<00:22, 175917.83it/s]
end{sphinxVerbatim}
20%|██ | 1000897/4997817 [00:05<00:22, 175408.44it/s]
+20%|█▉ | 993657/4997817 [00:05<00:22, 175917.83it/s]
+- 20%|██ | 1011277/4997817 [00:05<00:22, 175998.51it/s]
- -
</pre>
- 20%|██ | 1018439/4997817 [00:05<00:22, 175241.58it/s]
- 20%|██ | 1011277/4997817 [00:05<00:22, 175998.51it/s]
end{sphinxVerbatim}
20%|██ | 1018439/4997817 [00:05<00:22, 175241.58it/s]
+20%|██ | 1011277/4997817 [00:05<00:22, 175998.51it/s]
+- 21%|██ | 1028880/4997817 [00:05<00:22, 175962.99it/s]
- -
</pre>
- 21%|██ | 1036049/4997817 [00:05<00:22, 175496.75it/s]
- 21%|██ | 1028880/4997817 [00:05<00:22, 175962.99it/s]
end{sphinxVerbatim}
21%|██ | 1036049/4997817 [00:05<00:22, 175496.75it/s]
+21%|██ | 1028880/4997817 [00:05<00:22, 175962.99it/s]
+- 21%|██ | 1046537/4997817 [00:06<00:22, 176142.71it/s]
- -
</pre>
- 21%|██ | 1053831/4997817 [00:06<00:22, 176189.86it/s]
- 21%|██ | 1046537/4997817 [00:06<00:22, 176142.71it/s]
end{sphinxVerbatim}
21%|██ | 1053831/4997817 [00:06<00:22, 176189.86it/s]
+21%|██ | 1046537/4997817 [00:06<00:22, 176142.71it/s]
+- 21%|██▏ | 1064153/4997817 [00:06<00:22, 176017.85it/s]
- -
</pre>
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end{sphinxVerbatim}
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</pre>
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</pre>
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end{sphinxVerbatim}
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</pre>
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</pre>
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end{sphinxVerbatim}
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</pre>
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end{sphinxVerbatim}
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end{sphinxVerbatim}
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</pre>
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end{sphinxVerbatim}
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</pre>
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end{sphinxVerbatim}
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</pre>
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</pre>
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</pre>
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</pre>
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Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().
This dataset has 10 classes.
-Classes: {'cancel_transfer', 'supported_cards_and_currencies', 'visa_or_mastercard', 'card_about_to_expire', 'apple_pay_or_google_pay', 'getting_spare_card', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'change_pin', 'card_payment_fee_charged'}
+Classes: {'cancel_transfer', 'supported_cards_and_currencies', 'card_about_to_expire', 'change_pin', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'getting_spare_card', 'card_payment_fee_charged', 'visa_or_mastercard', 'beneficiary_not_allowed'}
Let’s print the first example in the train set.
diff --git a/master/tutorials/text.ipynb b/master/tutorials/text.ipynb index f324d2241..30e708f03 100644 --- a/master/tutorials/text.ipynb +++ b/master/tutorials/text.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:26.445820Z", - "iopub.status.busy": "2024-01-16T18:26:26.445377Z", - "iopub.status.idle": "2024-01-16T18:26:28.495593Z", - "shell.execute_reply": "2024-01-16T18:26:28.494982Z" + "iopub.execute_input": "2024-01-17T17:58:04.452112Z", + "iopub.status.busy": "2024-01-17T17:58:04.451920Z", + "iopub.status.idle": "2024-01-17T17:58:06.513490Z", + "shell.execute_reply": "2024-01-17T17:58:06.512794Z" }, "nbsphinx": "hidden" }, @@ -134,7 +134,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:28.498491Z", - "iopub.status.busy": "2024-01-16T18:26:28.498003Z", - "iopub.status.idle": "2024-01-16T18:26:28.501599Z", - "shell.execute_reply": "2024-01-16T18:26:28.501090Z" + "iopub.execute_input": "2024-01-17T17:58:06.516595Z", + "iopub.status.busy": "2024-01-17T17:58:06.516197Z", + "iopub.status.idle": "2024-01-17T17:58:06.519890Z", + "shell.execute_reply": "2024-01-17T17:58:06.519295Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:28.503791Z", - "iopub.status.busy": "2024-01-16T18:26:28.503503Z", - "iopub.status.idle": "2024-01-16T18:26:28.506652Z", - "shell.execute_reply": "2024-01-16T18:26:28.506135Z" + "iopub.execute_input": "2024-01-17T17:58:06.522149Z", + "iopub.status.busy": "2024-01-17T17:58:06.521812Z", + "iopub.status.idle": "2024-01-17T17:58:06.525082Z", + "shell.execute_reply": "2024-01-17T17:58:06.524478Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:28.508928Z", - "iopub.status.busy": "2024-01-16T18:26:28.508626Z", - "iopub.status.idle": "2024-01-16T18:26:28.557457Z", - "shell.execute_reply": "2024-01-16T18:26:28.556849Z" + "iopub.execute_input": "2024-01-17T17:58:06.527397Z", + "iopub.status.busy": "2024-01-17T17:58:06.527051Z", + "iopub.status.idle": "2024-01-17T17:58:06.696915Z", + "shell.execute_reply": "2024-01-17T17:58:06.696267Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:28.559740Z", - "iopub.status.busy": "2024-01-16T18:26:28.559386Z", - "iopub.status.idle": "2024-01-16T18:26:28.563106Z", - "shell.execute_reply": "2024-01-16T18:26:28.562607Z" + "iopub.execute_input": "2024-01-17T17:58:06.699410Z", + "iopub.status.busy": "2024-01-17T17:58:06.698895Z", + "iopub.status.idle": "2024-01-17T17:58:06.702802Z", + "shell.execute_reply": "2024-01-17T17:58:06.702205Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:28.565464Z", - "iopub.status.busy": "2024-01-16T18:26:28.565115Z", - "iopub.status.idle": "2024-01-16T18:26:28.568664Z", - "shell.execute_reply": "2024-01-16T18:26:28.568087Z" + "iopub.execute_input": "2024-01-17T17:58:06.705199Z", + "iopub.status.busy": "2024-01-17T17:58:06.704700Z", + "iopub.status.idle": "2024-01-17T17:58:06.708685Z", + "shell.execute_reply": "2024-01-17T17:58:06.708051Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'cancel_transfer', 'supported_cards_and_currencies', 'visa_or_mastercard', 'card_about_to_expire', 'apple_pay_or_google_pay', 'getting_spare_card', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'change_pin', 'card_payment_fee_charged'}\n" + "Classes: {'cancel_transfer', 'supported_cards_and_currencies', 'card_about_to_expire', 'change_pin', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'getting_spare_card', 'card_payment_fee_charged', 'visa_or_mastercard', 'beneficiary_not_allowed'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:28.571005Z", - "iopub.status.busy": "2024-01-16T18:26:28.570638Z", - "iopub.status.idle": "2024-01-16T18:26:28.574416Z", - "shell.execute_reply": "2024-01-16T18:26:28.573895Z" + "iopub.execute_input": "2024-01-17T17:58:06.711074Z", + "iopub.status.busy": "2024-01-17T17:58:06.710625Z", + "iopub.status.idle": "2024-01-17T17:58:06.714277Z", + "shell.execute_reply": "2024-01-17T17:58:06.713674Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:28.576801Z", - "iopub.status.busy": "2024-01-16T18:26:28.576438Z", - "iopub.status.idle": "2024-01-16T18:26:28.580004Z", - "shell.execute_reply": "2024-01-16T18:26:28.579461Z" + "iopub.execute_input": "2024-01-17T17:58:06.716551Z", + "iopub.status.busy": "2024-01-17T17:58:06.716194Z", + "iopub.status.idle": "2024-01-17T17:58:06.719743Z", + "shell.execute_reply": "2024-01-17T17:58:06.719140Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:28.582254Z", - "iopub.status.busy": "2024-01-16T18:26:28.581918Z", - "iopub.status.idle": "2024-01-16T18:26:37.142048Z", - "shell.execute_reply": "2024-01-16T18:26:37.141403Z" + "iopub.execute_input": "2024-01-17T17:58:06.722197Z", + "iopub.status.busy": "2024-01-17T17:58:06.721848Z", + "iopub.status.idle": "2024-01-17T17:58:15.903407Z", + "shell.execute_reply": "2024-01-17T17:58:15.902766Z" } }, "outputs": [ @@ -502,10 +502,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:37.145360Z", - "iopub.status.busy": "2024-01-16T18:26:37.144923Z", - "iopub.status.idle": "2024-01-16T18:26:37.148171Z", - "shell.execute_reply": "2024-01-16T18:26:37.147656Z" + "iopub.execute_input": "2024-01-17T17:58:15.906850Z", + "iopub.status.busy": "2024-01-17T17:58:15.906363Z", + "iopub.status.idle": "2024-01-17T17:58:15.909663Z", + "shell.execute_reply": "2024-01-17T17:58:15.909117Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:37.150527Z", - "iopub.status.busy": "2024-01-16T18:26:37.150155Z", - "iopub.status.idle": "2024-01-16T18:26:37.153089Z", - "shell.execute_reply": "2024-01-16T18:26:37.152539Z" + "iopub.execute_input": "2024-01-17T17:58:15.911984Z", + "iopub.status.busy": "2024-01-17T17:58:15.911780Z", + "iopub.status.idle": "2024-01-17T17:58:15.914788Z", + "shell.execute_reply": "2024-01-17T17:58:15.914152Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:37.155247Z", - "iopub.status.busy": "2024-01-16T18:26:37.154870Z", - "iopub.status.idle": "2024-01-16T18:26:39.352948Z", - "shell.execute_reply": "2024-01-16T18:26:39.352038Z" + "iopub.execute_input": "2024-01-17T17:58:15.917364Z", + "iopub.status.busy": "2024-01-17T17:58:15.916868Z", + "iopub.status.idle": "2024-01-17T17:58:18.153348Z", + "shell.execute_reply": "2024-01-17T17:58:18.152612Z" }, "scrolled": true }, @@ -571,10 +571,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:39.356461Z", - "iopub.status.busy": "2024-01-16T18:26:39.355758Z", - "iopub.status.idle": "2024-01-16T18:26:39.363742Z", - "shell.execute_reply": "2024-01-16T18:26:39.363219Z" + "iopub.execute_input": "2024-01-17T17:58:18.157203Z", + "iopub.status.busy": "2024-01-17T17:58:18.156132Z", + "iopub.status.idle": "2024-01-17T17:58:18.164623Z", + "shell.execute_reply": "2024-01-17T17:58:18.164085Z" } }, "outputs": [ @@ -675,10 +675,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:39.366319Z", - "iopub.status.busy": "2024-01-16T18:26:39.365936Z", - "iopub.status.idle": "2024-01-16T18:26:39.370077Z", - "shell.execute_reply": "2024-01-16T18:26:39.369561Z" + "iopub.execute_input": "2024-01-17T17:58:18.167048Z", + "iopub.status.busy": "2024-01-17T17:58:18.166666Z", + "iopub.status.idle": "2024-01-17T17:58:18.171012Z", + "shell.execute_reply": "2024-01-17T17:58:18.170356Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:39.372290Z", - "iopub.status.busy": "2024-01-16T18:26:39.371921Z", - "iopub.status.idle": "2024-01-16T18:26:39.375337Z", - "shell.execute_reply": "2024-01-16T18:26:39.374711Z" + "iopub.execute_input": "2024-01-17T17:58:18.173331Z", + "iopub.status.busy": "2024-01-17T17:58:18.172904Z", + "iopub.status.idle": "2024-01-17T17:58:18.176499Z", + "shell.execute_reply": "2024-01-17T17:58:18.175881Z" } }, "outputs": [ @@ -730,10 +730,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:39.377705Z", - "iopub.status.busy": "2024-01-16T18:26:39.377329Z", - "iopub.status.idle": "2024-01-16T18:26:39.380543Z", - "shell.execute_reply": "2024-01-16T18:26:39.380000Z" + "iopub.execute_input": "2024-01-17T17:58:18.178843Z", + "iopub.status.busy": "2024-01-17T17:58:18.178637Z", + "iopub.status.idle": "2024-01-17T17:58:18.181882Z", + "shell.execute_reply": "2024-01-17T17:58:18.181359Z" } }, "outputs": [], @@ -753,10 +753,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:39.382924Z", - "iopub.status.busy": "2024-01-16T18:26:39.382422Z", - "iopub.status.idle": "2024-01-16T18:26:39.390013Z", - "shell.execute_reply": "2024-01-16T18:26:39.389515Z" + "iopub.execute_input": "2024-01-17T17:58:18.184292Z", + "iopub.status.busy": "2024-01-17T17:58:18.183941Z", + "iopub.status.idle": "2024-01-17T17:58:18.191627Z", + "shell.execute_reply": "2024-01-17T17:58:18.191112Z" } }, "outputs": [ @@ -881,10 +881,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:39.392353Z", - "iopub.status.busy": "2024-01-16T18:26:39.392147Z", - "iopub.status.idle": "2024-01-16T18:26:39.634627Z", - "shell.execute_reply": "2024-01-16T18:26:39.633997Z" + "iopub.execute_input": "2024-01-17T17:58:18.194271Z", + "iopub.status.busy": "2024-01-17T17:58:18.193907Z", + "iopub.status.idle": "2024-01-17T17:58:18.459352Z", + "shell.execute_reply": "2024-01-17T17:58:18.458616Z" }, "scrolled": true }, @@ -923,10 +923,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:39.637681Z", - "iopub.status.busy": "2024-01-16T18:26:39.637242Z", - "iopub.status.idle": "2024-01-16T18:26:39.914774Z", - "shell.execute_reply": "2024-01-16T18:26:39.914171Z" + "iopub.execute_input": "2024-01-17T17:58:18.463589Z", + "iopub.status.busy": "2024-01-17T17:58:18.462420Z", + "iopub.status.idle": "2024-01-17T17:58:18.740920Z", + "shell.execute_reply": "2024-01-17T17:58:18.740191Z" }, "scrolled": true }, @@ -959,10 +959,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:39.917765Z", - "iopub.status.busy": "2024-01-16T18:26:39.917338Z", - "iopub.status.idle": "2024-01-16T18:26:39.921453Z", - "shell.execute_reply": "2024-01-16T18:26:39.920861Z" + "iopub.execute_input": "2024-01-17T17:58:18.745764Z", + "iopub.status.busy": "2024-01-17T17:58:18.744581Z", + "iopub.status.idle": "2024-01-17T17:58:18.750295Z", + "shell.execute_reply": "2024-01-17T17:58:18.749695Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 52e392f6a..871ae8560 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -870,7 +870,7 @@
---2024-01-16 18:26:44-- https://data.deepai.org/conll2003.zip
+--2024-01-17 17:58:23-- https://data.deepai.org/conll2003.zip
Resolving data.deepai.org (data.deepai.org)...
-185.93.1.247, 2400:52e0:1a00::941:1
-Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... connected.
-
-HTTP request sent, awaiting response...
-
-200 OK
+143.244.50.91, 2400:52e0:1a01::899:1
+Connecting to data.deepai.org (data.deepai.org)|143.244.50.91|:443... connected.
+HTTP request sent, awaiting response... 200 OK
Length: 982975 (960K) [application/zip]
Saving to: ‘conll2003.zip’conll2003.zip 100%[===================>] 959.94K 5.55MB/s in 0.2s
+conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.05s
--2024-01-16 18:26:45 (5.55 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-01-17 17:58:23 (17.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]
mkdir: cannot create directory ‘data’: File exists </pre>
conll2003.zip 100%[===================>] 959.94K 5.55MB/s in 0.2s
+conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.05s
--2024-01-16 18:26:45 (5.55 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-01-17 17:58:23 (17.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]
mkdir: cannot create directory ‘data’: File exists end{sphinxVerbatim}
conll2003.zip 100%[===================>] 959.94K 5.55MB/s in 0.2s
+conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.05s
-@@ -954,8 +940,17 @@+1. Install required dependencies and download data
+- - inflating: data/valid.txt +--2024-01-17 17:58:24-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz +Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.40.201, 52.217.104.68, 52.217.165.25, ... +Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.40.201|:443... ++++++connected.@@ -963,9 +958,6 @@@@ -996,39 +988,59 @@1. Install required dependencies and download data
---2024-01-16 18:26:45-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz -Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.171.89, 52.216.37.153, 54.231.192.249, ... -Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.171.89|:443... connected. HTTP request sent, awaiting response...1. Install required dependencies and download data
-
+- pred_probs.npz 67%[============> ] 10.93M 54.7MB/s
- pred_probs.npz 1%[ ] 262.53K 1.15MB/s
- +
++</pre>
+- pred_probs.npz 1%[ ] 262.53K 1.15MB/s
- +
++end{sphinxVerbatim}
+pred_probs.npz 1%[ ] 262.53K 1.15MB/s
++++++++
-- pred_probs.npz 27%[====> ] 4.51M 10.1MB/s
- -
</pre>
- pred_probs.npz 67%[============> ] 10.93M 54.7MB/s
- pred_probs.npz 27%[====> ] 4.51M 10.1MB/s
end{sphinxVerbatim}
pred_probs.npz 67%[============> ] 10.93M 54.7MB/s
+pred_probs.npz 27%[====> ] 4.51M 10.1MB/s
-pred_probs.npz 100%[===================>] 16.26M 61.5MB/s in 0.3s
+pred_probs.npz 98%[==================> ] 16.07M 23.9MB/s +pred_probs.npz 100%[===================>] 16.26M 24.2MB/s in 0.7s
--2024-01-16 18:26:45 (61.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
+2024-01-17 17:58:25 (24.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
</pre>
pred_probs.npz 100%[===================>] 16.26M 61.5MB/s in 0.3s
+pred_probs.npz 98%[==================> ] 16.07M 23.9MB/s +pred_probs.npz 100%[===================>] 16.26M 24.2MB/s in 0.7s
--2024-01-16 18:26:45 (61.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
+2024-01-17 17:58:25 (24.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
end{sphinxVerbatim}
pred_probs.npz 100%[===================>] 16.26M 61.5MB/s in 0.3s
+pred_probs.npz 98%[==================> ] 16.07M 23.9MB/s +pred_probs.npz 100%[===================>] 16.26M 24.2MB/s in 0.7s
-2024-01-16 18:26:45 (61.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
+2024-01-17 17:58:25 (24.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
[3]: diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index d03f7c050..9243d7ad9 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:44.432822Z", - "iopub.status.busy": "2024-01-16T18:26:44.432609Z", - "iopub.status.idle": "2024-01-16T18:26:46.060279Z", - "shell.execute_reply": "2024-01-16T18:26:46.059587Z" + "iopub.execute_input": "2024-01-17T17:58:23.424165Z", + "iopub.status.busy": "2024-01-17T17:58:23.423975Z", + "iopub.status.idle": "2024-01-17T17:58:25.202201Z", + "shell.execute_reply": "2024-01-17T17:58:25.201433Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-01-16 18:26:44-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-01-17 17:58:23-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,22 +94,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.247, 2400:52e0:1a00::941:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... connected.\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "HTTP request sent, awaiting response... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200 OK\r\n", + "143.244.50.91, 2400:52e0:1a01::899:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|143.244.50.91|:443... connected.\r\n", + "HTTP request sent, awaiting response... 200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -122,9 +109,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K 5.55MB/s in 0.2s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.05s \r\n", "\r\n", - "2024-01-16 18:26:45 (5.55 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-01-17 17:58:23 (17.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -136,24 +123,30 @@ "Archive: conll2003.zip\r\n", " inflating: data/metadata \r\n", " inflating: data/test.txt \r\n", - " inflating: data/train.txt " + " inflating: data/train.txt \r\n", + " inflating: data/valid.txt \r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "\r\n", - " inflating: data/valid.txt \r\n" + "--2024-01-17 17:58:24-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.40.201, 52.217.104.68, 52.217.165.25, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.40.201|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "--2024-01-16 18:26:45-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.171.89, 52.216.37.153, 54.231.192.249, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.171.89|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,7 +167,15 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 67%[============> ] 10.93M 54.7MB/s " + "pred_probs.npz 1%[ ] 262.53K 1.15MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 27%[====> ] 4.51M 10.1MB/s " ] }, { @@ -182,9 +183,10 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 61.5MB/s in 0.3s \r\n", + "pred_probs.npz 98%[==================> ] 16.07M 23.9MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 24.2MB/s in 0.7s \r\n", "\r\n", - "2024-01-16 18:26:45 (61.5 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-01-17 17:58:25 (24.2 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -201,10 +203,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:46.062757Z", - "iopub.status.busy": "2024-01-16T18:26:46.062552Z", - "iopub.status.idle": "2024-01-16T18:26:47.073438Z", - "shell.execute_reply": "2024-01-16T18:26:47.072752Z" + "iopub.execute_input": "2024-01-17T17:58:25.205535Z", + "iopub.status.busy": "2024-01-17T17:58:25.205105Z", + "iopub.status.idle": "2024-01-17T17:58:26.262369Z", + "shell.execute_reply": "2024-01-17T17:58:26.261719Z" }, "nbsphinx": "hidden" }, @@ -215,7 +217,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@a8d2170f0db89b804931917dd930161c971bea94\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@15bec56103a268b4cbc829af93459cd8a66649de\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -241,10 +243,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:47.076275Z", - "iopub.status.busy": "2024-01-16T18:26:47.075796Z", - "iopub.status.idle": "2024-01-16T18:26:47.079464Z", - "shell.execute_reply": "2024-01-16T18:26:47.078949Z" + "iopub.execute_input": "2024-01-17T17:58:26.265615Z", + "iopub.status.busy": "2024-01-17T17:58:26.265000Z", + "iopub.status.idle": "2024-01-17T17:58:26.268761Z", + "shell.execute_reply": "2024-01-17T17:58:26.268164Z" } }, "outputs": [], @@ -294,10 +296,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:47.081729Z", - "iopub.status.busy": "2024-01-16T18:26:47.081527Z", - "iopub.status.idle": "2024-01-16T18:26:47.084579Z", - "shell.execute_reply": "2024-01-16T18:26:47.084042Z" + "iopub.execute_input": "2024-01-17T17:58:26.271286Z", + "iopub.status.busy": "2024-01-17T17:58:26.270877Z", + "iopub.status.idle": "2024-01-17T17:58:26.274169Z", + "shell.execute_reply": "2024-01-17T17:58:26.273608Z" }, "nbsphinx": "hidden" }, @@ -315,10 +317,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:47.086724Z", - "iopub.status.busy": "2024-01-16T18:26:47.086524Z", - "iopub.status.idle": "2024-01-16T18:26:54.928199Z", - "shell.execute_reply": "2024-01-16T18:26:54.927577Z" + "iopub.execute_input": "2024-01-17T17:58:26.276506Z", + "iopub.status.busy": "2024-01-17T17:58:26.276193Z", + "iopub.status.idle": "2024-01-17T17:58:34.303876Z", + "shell.execute_reply": "2024-01-17T17:58:34.303255Z" } }, "outputs": [], @@ -392,10 +394,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:54.931044Z", - "iopub.status.busy": "2024-01-16T18:26:54.930670Z", - "iopub.status.idle": "2024-01-16T18:26:54.936757Z", - "shell.execute_reply": "2024-01-16T18:26:54.936221Z" + "iopub.execute_input": "2024-01-17T17:58:34.306724Z", + "iopub.status.busy": "2024-01-17T17:58:34.306333Z", + "iopub.status.idle": "2024-01-17T17:58:34.312260Z", + "shell.execute_reply": "2024-01-17T17:58:34.311721Z" }, "nbsphinx": "hidden" }, @@ -435,10 +437,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:54.938947Z", - "iopub.status.busy": "2024-01-16T18:26:54.938584Z", - "iopub.status.idle": "2024-01-16T18:26:55.375633Z", - "shell.execute_reply": "2024-01-16T18:26:55.374948Z" + "iopub.execute_input": "2024-01-17T17:58:34.314518Z", + "iopub.status.busy": "2024-01-17T17:58:34.314319Z", + "iopub.status.idle": "2024-01-17T17:58:34.744940Z", + "shell.execute_reply": "2024-01-17T17:58:34.744292Z" } }, "outputs": [], @@ -475,10 +477,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:55.378776Z", - "iopub.status.busy": "2024-01-16T18:26:55.378226Z", - "iopub.status.idle": "2024-01-16T18:26:55.384946Z", - "shell.execute_reply": "2024-01-16T18:26:55.384227Z" + "iopub.execute_input": "2024-01-17T17:58:34.747755Z", + "iopub.status.busy": "2024-01-17T17:58:34.747534Z", + "iopub.status.idle": "2024-01-17T17:58:34.753183Z", + "shell.execute_reply": "2024-01-17T17:58:34.752676Z" } }, "outputs": [ @@ -550,10 +552,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:55.387617Z", - "iopub.status.busy": "2024-01-16T18:26:55.387265Z", - "iopub.status.idle": "2024-01-16T18:26:57.330447Z", - "shell.execute_reply": "2024-01-16T18:26:57.329532Z" + "iopub.execute_input": "2024-01-17T17:58:34.755736Z", + "iopub.status.busy": "2024-01-17T17:58:34.755375Z", + "iopub.status.idle": "2024-01-17T17:58:36.724878Z", + "shell.execute_reply": "2024-01-17T17:58:36.723947Z" } }, "outputs": [], @@ -575,10 +577,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:57.334192Z", - "iopub.status.busy": "2024-01-16T18:26:57.333377Z", - "iopub.status.idle": "2024-01-16T18:26:57.340257Z", - "shell.execute_reply": "2024-01-16T18:26:57.339626Z" + "iopub.execute_input": "2024-01-17T17:58:36.730399Z", + "iopub.status.busy": "2024-01-17T17:58:36.727865Z", + "iopub.status.idle": "2024-01-17T17:58:36.735089Z", + "shell.execute_reply": "2024-01-17T17:58:36.734419Z" } }, "outputs": [ @@ -614,10 +616,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:57.342851Z", - "iopub.status.busy": "2024-01-16T18:26:57.342436Z", - "iopub.status.idle": "2024-01-16T18:26:57.360032Z", - "shell.execute_reply": "2024-01-16T18:26:57.359548Z" + "iopub.execute_input": "2024-01-17T17:58:36.737747Z", + "iopub.status.busy": "2024-01-17T17:58:36.737272Z", + "iopub.status.idle": "2024-01-17T17:58:36.761752Z", + "shell.execute_reply": "2024-01-17T17:58:36.761145Z" } }, "outputs": [ @@ -795,10 +797,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:57.362505Z", - "iopub.status.busy": "2024-01-16T18:26:57.362141Z", - "iopub.status.idle": "2024-01-16T18:26:57.393875Z", - "shell.execute_reply": "2024-01-16T18:26:57.393317Z" + "iopub.execute_input": "2024-01-17T17:58:36.764355Z", + "iopub.status.busy": "2024-01-17T17:58:36.764016Z", + "iopub.status.idle": "2024-01-17T17:58:36.799290Z", + "shell.execute_reply": "2024-01-17T17:58:36.798797Z" } }, "outputs": [ @@ -900,10 +902,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:57.396419Z", - "iopub.status.busy": "2024-01-16T18:26:57.396040Z", - "iopub.status.idle": "2024-01-16T18:26:57.404752Z", - "shell.execute_reply": "2024-01-16T18:26:57.404255Z" + "iopub.execute_input": "2024-01-17T17:58:36.801809Z", + "iopub.status.busy": "2024-01-17T17:58:36.801371Z", + "iopub.status.idle": "2024-01-17T17:58:36.809794Z", + "shell.execute_reply": "2024-01-17T17:58:36.809297Z" } }, "outputs": [ @@ -977,10 +979,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:57.406914Z", - "iopub.status.busy": "2024-01-16T18:26:57.406703Z", - "iopub.status.idle": "2024-01-16T18:26:59.250819Z", - "shell.execute_reply": "2024-01-16T18:26:59.250197Z" + "iopub.execute_input": "2024-01-17T17:58:36.812236Z", + "iopub.status.busy": "2024-01-17T17:58:36.811808Z", + "iopub.status.idle": "2024-01-17T17:58:38.694852Z", + "shell.execute_reply": "2024-01-17T17:58:38.694278Z" } }, "outputs": [ @@ -1152,10 +1154,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-01-16T18:26:59.253304Z", - "iopub.status.busy": "2024-01-16T18:26:59.253057Z", - "iopub.status.idle": "2024-01-16T18:26:59.257459Z", - "shell.execute_reply": "2024-01-16T18:26:59.256934Z" + "iopub.execute_input": "2024-01-17T17:58:38.697532Z", + "iopub.status.busy": "2024-01-17T17:58:38.697105Z", + "iopub.status.idle": "2024-01-17T17:58:38.701595Z", + "shell.execute_reply": "2024-01-17T17:58:38.701051Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index 11eac3d19..c37486deb 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.5.0", - commit_hash: "a8d2170f0db89b804931917dd930161c971bea94", + commit_hash: "15bec56103a268b4cbc829af93459cd8a66649de", }; \ No newline at end of file