diff --git a/.doctrees/_notebooks/Basic_usage.doctree b/.doctrees/_notebooks/Basic_usage.doctree index 3510506..4f56556 100644 Binary files a/.doctrees/_notebooks/Basic_usage.doctree and b/.doctrees/_notebooks/Basic_usage.doctree differ diff --git a/.doctrees/_notebooks/Tutorial.doctree b/.doctrees/_notebooks/Tutorial.doctree index 30601cb..5a8cc15 100644 Binary files a/.doctrees/_notebooks/Tutorial.doctree and b/.doctrees/_notebooks/Tutorial.doctree differ diff --git a/.doctrees/_notebooks/Using_redflag_with_Pandas.doctree b/.doctrees/_notebooks/Using_redflag_with_Pandas.doctree index 91864cc..12d9c93 100644 Binary files a/.doctrees/_notebooks/Using_redflag_with_Pandas.doctree and b/.doctrees/_notebooks/Using_redflag_with_Pandas.doctree differ diff --git a/.doctrees/_notebooks/Using_redflag_with_sklearn.doctree b/.doctrees/_notebooks/Using_redflag_with_sklearn.doctree index 5fcbcea..215c071 100644 Binary files a/.doctrees/_notebooks/Using_redflag_with_sklearn.doctree and b/.doctrees/_notebooks/Using_redflag_with_sklearn.doctree differ diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle index aacb62d..9e0a4c2 100644 Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ diff --git a/_images/74d2d18146189daf45b1ee624ebeb7cd87cb427a8ee1b9a267a1ae8870cbaaa6.png b/_images/74d2d18146189daf45b1ee624ebeb7cd87cb427a8ee1b9a267a1ae8870cbaaa6.png deleted file mode 100644 index 6b92aea..0000000 Binary files a/_images/74d2d18146189daf45b1ee624ebeb7cd87cb427a8ee1b9a267a1ae8870cbaaa6.png and /dev/null differ diff --git a/_images/c565f9dff80f5405d7d514d7c2afe2f4941fe1c6fb1ddecc4458ba39f014f3bd.png b/_images/c565f9dff80f5405d7d514d7c2afe2f4941fe1c6fb1ddecc4458ba39f014f3bd.png new file mode 100644 index 0000000..15c8936 Binary files /dev/null and b/_images/c565f9dff80f5405d7d514d7c2afe2f4941fe1c6fb1ddecc4458ba39f014f3bd.png differ diff --git a/_images/d78e85605ed7b0b7927b040c65b314bea5f357944ad18bb72003f8c4b14217bf.png b/_images/d78e85605ed7b0b7927b040c65b314bea5f357944ad18bb72003f8c4b14217bf.png deleted file mode 100644 index 04e5989..0000000 Binary files a/_images/d78e85605ed7b0b7927b040c65b314bea5f357944ad18bb72003f8c4b14217bf.png and /dev/null differ diff --git a/_notebooks/Basic_usage.html b/_notebooks/Basic_usage.html index 07eda8c..122b699 100644 --- a/_notebooks/Basic_usage.html +++ b/_notebooks/Basic_usage.html @@ -254,7 +254,7 @@

🚩 Basic usage -
'0.4.2rc1'
+
'0.4.2'
@@ -560,7 +560,7 @@

Imbalance metrics -
<matplotlib.lines.Line2D at 0x7f8da2fd35c0>
+
<matplotlib.lines.Line2D at 0x7fe6b127ae10>
 

 

 ../_images/7a974bfe7d9f6b4603aa654c27a8c93be2894b1b6223c36622d724375dfd18ae.png
@@ -616,7 +616,7 @@ 
Outliers
-
<seaborn.axisgrid.JointGrid at 0x7f8da12c57f0>
+
<seaborn.axisgrid.JointGrid at 0x7fe6b06cd340>
 

 

 ../_images/73a34d2ac46eb28b08f01358c3bc001af1f180ad4ba7474734205a481c180835.png
@@ -671,10 +671,10 @@ 
Outliers
-
<seaborn.axisgrid.FacetGrid at 0x7f8da2565b50>
+
<seaborn.axisgrid.FacetGrid at 0x7fe6b0501070>
 

 

-../_images/74d2d18146189daf45b1ee624ebeb7cd87cb427a8ee1b9a267a1ae8870cbaaa6.png
+../_images/c565f9dff80f5405d7d514d7c2afe2f4941fe1c6fb1ddecc4458ba39f014f3bd.png

This truncated normal distribution has no outliers (there are only about 60, compared to the 100 we expect at this confidence level of 99% on this dataset of 10,000 records).

@@ -715,7 +715,7 @@

Clipping

-
<seaborn.axisgrid.FacetGrid at 0x7f8da12554c0>
+
<seaborn.axisgrid.FacetGrid at 0x7fe6b03e67b0>
 

 

 ../_images/5488f73cdf034061545c78f25fe27c514af3927c350d04e04bbee7c2b07662e0.png
@@ -772,7 +772,7 @@ 
Distribution shape
-
<seaborn.axisgrid.FacetGrid at 0x7f8da016a060>
+
<seaborn.axisgrid.FacetGrid at 0x7fe6b0424da0>
 

 

 ../_images/07bc624e812626af7a9a216f8aa7b233e9130872991e9e5a1b2c98f0be17d7a4.png
@@ -921,7 +921,7 @@ 
Feature importance
-
array([0.36923691, 0.31790105, 0.22145207, 0.08204644, 0.        ])
+
array([0.41621216, 0.2725526 , 0.23427556, 0.07695968, 0.        ])
 

 

 

@@ -966,7 +966,7 @@ 
Feature importance
-
array([0.09514279, 0.35815121, 0.52083108, 0.02587492, 0.        ])
+
array([0.09713124, 0.36666505, 0.50735191, 0.0288518 , 0.        ])
 

 

 

diff --git a/_notebooks/Tutorial.html b/_notebooks/Tutorial.html
index 2d45f7d..bfe5052 100644
--- a/_notebooks/Tutorial.html
+++ b/_notebooks/Tutorial.html
@@ -305,48 +305,7 @@ 
A simple ML workflow
 

 

-
---------------------------------------------------------------------------
-ValueError                                Traceback (most recent call last)
-Cell In[3], line 11
-      8 X_train_scaled = scaler.transform(X_train)
-      9 X_test_scaled = scaler.transform(X_test)
----> 11 clf.fit(X_train_scaled, y_train)
-     12 clf.predict(X_test_scaled)
-
-File /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/sklearn/base.py:1152, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
-   1145     estimator._validate_params()
-   1147 with config_context(
-   1148     skip_parameter_validation=(
-   1149         prefer_skip_nested_validation or global_skip_validation
-   1150     )
-   1151 ):
--> 1152     return fit_method(estimator, *args, **kwargs)
-
-File /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/sklearn/svm/_base.py:199, in BaseLibSVM.fit(self, X, y, sample_weight)
-    189 else:
-    190     X, y = self._validate_data(
-    191         X,
-    192         y,
-   (...)
-    196         accept_large_sparse=False,
-    197     )
---> 199 y = self._validate_targets(y)
-    201 sample_weight = np.asarray(
-    202     [] if sample_weight is None else sample_weight, dtype=np.float64
-    203 )
-    204 solver_type = LIBSVM_IMPL.index(self._impl)
-
-File /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/sklearn/svm/_base.py:747, in BaseSVC._validate_targets(self, y)
-    745 self.class_weight_ = compute_class_weight(self.class_weight, classes=cls, y=y_)
-    746 if len(cls) < 2:
---> 747     raise ValueError(
-    748         "The number of classes has to be greater than one; got %d class"
-    749         % len(cls)
-    750     )
-    752 self.classes_ = cls
-    754 return np.asarray(y, dtype=np.float64, order="C")
-
-ValueError: The number of classes has to be greater than one; got 1 class
+
array(['ss', 'ms'], dtype='<U2')
 

 

 

@@ -372,7 +331,7 @@ 
A quick look at 
 

 

-
'0.3.1.dev18+g743d11f.d20230927'
+
'0.4.2'
 

 

 

@@ -620,15 +579,10 @@ 
Clipping
 

 

 

-
is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
-use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
-The figure layout has changed to tight
-

-

-
<seaborn.axisgrid.FacetGrid at 0x7efc2f1dff10>
+
<seaborn.axisgrid.FacetGrid at 0x7f153fc84830>
 

 

-../_images/d78e85605ed7b0b7927b040c65b314bea5f357944ad18bb72003f8c4b14217bf.png
+../_images/5488f73cdf034061545c78f25fe27c514af3927c350d04e04bbee7c2b07662e0.png
 

 

 
@@ -678,7 +632,7 @@ 
Importance
-
array([0.41495144, 0.20256686, 0.3156612 , 0.0668205 ])
+
array([0.40996915, 0.20901374, 0.31748025, 0.06353685])
 

 

 

@@ -779,10 +733,14 @@ 
Pipelines
🚩 There are more outliers than expected in the training data (316 vs 31).
+

+

+
🚩 Feature 3 has low importance; check for relevance.
+ℹ️ Dummy classifier scores: {'f1': 0.25488459423559595, 'roc_auc': 0.5} (most_frequent strategy).
 

 

 

 
Pipeline(steps=[('detector',
-                 Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7efc26efbf60>,
+                 Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7f153c8e3d80>,
                           message='are negative')),
                 ('svc', SVC())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

 

 
The noise feature we added has negative values; the others are all positive, which is what we expect for these data.

diff --git a/_notebooks/Using_redflag_with_Pandas.html b/_notebooks/Using_redflag_with_Pandas.html
index ff4be60..bf1e362 100644
--- a/_notebooks/Using_redflag_with_Pandas.html
+++ b/_notebooks/Using_redflag_with_Pandas.html
@@ -247,7 +247,7 @@ 
🚩 Using redf
 

 

 

-
'0.4.2rc1'
+
'0.4.2'
 

 

 

@@ -443,8 +443,8 @@ 
Series accessor
-
{'f1': 0.24642601013909113,
- 'roc_auc': 0.5071664777645397,
+
{'f1': 0.2411344733492839,
+ 'roc_auc': 0.5030196416166594,
  'strategy': 'stratified',
  'task': 'classification'}
 

@@ -476,9 +476,9 @@ 
Series accessor
 
Continuous data suitable for regression
-Outliers:    [ 141  142  175  532  575  581  583  633  662  757  768  769  773  801
- 1316 1498 1547 1744 1745 1754 1756 1778 1779 1780 1784 1785 1788 1808
- 1809 1812 2884 2932 2973 2974 3004 3087 3094 3095 3100 3109]
+Outliers:    [  34   35  136  140  141  142  143  145  175  180  181  182  581  633
+  662  768  769  801 1316 1547 1731 1732 1744 1754 1756 1778 1779 1780
+ 1784 1788 1808 1812 2884 2973 2974 3004 3079 3080 3087 3109]
 Correlated:  True
 Dummy scores:{'mean': {'mean_squared_error': 47528.78263092096, 'r2': 0.0}}
 

@@ -498,7 +498,7 @@ 
DataFrame accessor
-
array([0.23175155, 0.21627564, 0.34215632, 0.20981648])
+
array([0.23155584, 0.21912608, 0.33738409, 0.21193399])
 

 

 

diff --git a/_notebooks/Using_redflag_with_sklearn.html b/_notebooks/Using_redflag_with_sklearn.html
index 345adf0..74d2ba2 100644
--- a/_notebooks/Using_redflag_with_sklearn.html
+++ b/_notebooks/Using_redflag_with_sklearn.html
@@ -581,7 +581,7 @@ 
Using the pre-built 
🚩 There are more outliers than expected in the training data (349 vs 31).
 

 

-
ℹ️ Dummy classifier scores: {'f1': 0.26207946089678574, 'roc_auc': 0.49778842936294404} (stratified strategy).
+
ℹ️ Dummy classifier scores: {'f1': 0.25488459423559595, 'roc_auc': 0.5} (most_frequent strategy).
 

 

 
Pipeline(steps=[('standardscaler', StandardScaler()),
@@ -750,7 +750,7 @@ 
The imbalance comparator
 
🚩 There is a different number of minority classes (2) compared to the training data (4).
-🚩 The minority classes (sandstone, dolomite) are different from those in the training data (sandstone, dolomite, wackestone, mudstone).
+🚩 The minority classes (sandstone, dolomite) are different from those in the training data (wackestone, sandstone, mudstone, dolomite).
 

 

 
array([[  66.276     , 2359.73324716,    3.591     ],
@@ -786,12 +786,12 @@ 
Making your own smoke detector
 
Pipeline(steps=[('detector',
-                 Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7fa46947d760>,
+                 Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7f9c489831a0>,
                           message='are NaNs')),
                 ('svc', SVC())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

 

 
There are no NaNs.

@@ -818,30 +818,30 @@ 
Making your own smoke detector
Pipeline(steps=[('standardscaler', StandardScaler()),
                 ('pipeline',
                  Pipeline(steps=[('detector-1',
-                                  Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7fa424977600>,
+                                  Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7f9c489822a0>,
                                            message='fail custom func '
                                                    'has_nans()')),
                                  ('detector-2',
-                                  Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7fa41db54fe0>,
+                                  Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7f9c489b4fe0>,
                                            message='fail custom func '
                                                    'has_outliers()'))])),
                 ('svc', SVC())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. 
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

 

 
diff --git a/reports/_notebooks/Tutorial.err.log b/reports/_notebooks/Tutorial.err.log
deleted file mode 100644
index 33ba613..0000000
--- a/reports/_notebooks/Tutorial.err.log
+++ /dev/null
@@ -1,78 +0,0 @@
-Traceback (most recent call last):
-  File "/opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution
-    executenb(
-  File "/opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/nbclient/client.py", line 1314, in execute
-    return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/jupyter_core/utils/__init__.py", line 173, in wrapped
-    return loop.run_until_complete(inner)
-           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/asyncio/base_events.py", line 664, in run_until_complete
-    return future.result()
-           ^^^^^^^^^^^^^^^
-  File "/opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/nbclient/client.py", line 709, in async_execute
-    await self.async_execute_cell(
-  File "/opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/nbclient/client.py", line 1062, in async_execute_cell
-    await self._check_raise_for_error(cell, cell_index, exec_reply)
-  File "/opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/nbclient/client.py", line 918, in _check_raise_for_error
-    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
-nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
-------------------
-from sklearn.model_selection import train_test_split
-
-scaler = StandardScaler()
-scaler.fit(X)
-
-X_train, X_test, y_train, y_test = train_test_split(X, y)
-
-X_train_scaled = scaler.transform(X_train)
-X_test_scaled = scaler.transform(X_test)
-
-clf.fit(X_train_scaled, y_train)
-clf.predict(X_test_scaled)
-------------------
-
-
----------------------------------------------------------------------------
-ValueError                                Traceback (most recent call last)
-Cell In[3], line 11
-      8 X_train_scaled = scaler.transform(X_train)
-      9 X_test_scaled = scaler.transform(X_test)
----> 11 clf.fit(X_train_scaled, y_train)
-     12 clf.predict(X_test_scaled)
-
-File /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/sklearn/base.py:1152, in _fit_context..decorator..wrapper(estimator, *args, **kwargs)
-   1145     estimator._validate_params()
-   1147 with config_context(
-   1148     skip_parameter_validation=(
-   1149         prefer_skip_nested_validation or global_skip_validation
-   1150     )
-   1151 ):
--> 1152     return fit_method(estimator, *args, **kwargs)
-
-File /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/sklearn/svm/_base.py:199, in BaseLibSVM.fit(self, X, y, sample_weight)
-    189 else:
-    190     X, y = self._validate_data(
-    191         X,
-    192         y,
-   (...)
-    196         accept_large_sparse=False,
-    197     )
---> 199 y = self._validate_targets(y)
-    201 sample_weight = np.asarray(
-    202     [] if sample_weight is None else sample_weight, dtype=np.float64
-    203 )
-    204 solver_type = LIBSVM_IMPL.index(self._impl)
-
-File /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/site-packages/sklearn/svm/_base.py:747, in BaseSVC._validate_targets(self, y)
-    745 self.class_weight_ = compute_class_weight(self.class_weight, classes=cls, y=y_)
-    746 if len(cls) < 2:
---> 747     raise ValueError(
-    748         "The number of classes has to be greater than one; got %d class"
-    749         % len(cls)
-    750     )
-    752 self.classes_ = cls
-    754 return np.asarray(y, dtype=np.float64, order="C")
-
-ValueError: The number of classes has to be greater than one; got 1 class
-
diff --git a/searchindex.js b/searchindex.js
index b536e9b..c92c772 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["_notebooks/Basic_usage", "_notebooks/Tutorial", "_notebooks/Using_redflag_with_Pandas", "_notebooks/Using_redflag_with_sklearn", "authors", "changelog", "contributing", "development", "index", "installation", "license", "redflag", "redflag.distributions", "redflag.imbalance", "redflag.importance", "redflag.independence", "redflag.markov", "redflag.outliers", "redflag.pandas", "redflag.sklearn", "redflag.target", "redflag.utils", "what_is_redflag"], "filenames": ["_notebooks/Basic_usage.ipynb", "_notebooks/Tutorial.ipynb", "_notebooks/Using_redflag_with_Pandas.ipynb", "_notebooks/Using_redflag_with_sklearn.ipynb", "authors.md", "changelog.md", "contributing.md", "development.md", "index.rst", "installation.md", "license.md", "redflag.rst", "redflag.distributions.rst", "redflag.imbalance.rst", "redflag.importance.rst", "redflag.independence.rst", "redflag.markov.rst", "redflag.outliers.rst", "redflag.pandas.rst", "redflag.sklearn.rst", "redflag.target.rst", "redflag.utils.rst", "what_is_redflag.md"], "titles": ["\ud83d\udea9 Basic usage", "\ud83d\udea9 Tutorial", "\ud83d\udea9 Using redflag with Pandas", "\ud83d\udea9 Using redflag with sklearn", "Authors", "Changelog", "Contributing", "Development", "Redflag: safer ML by design", "\ud83d\udea9 Installation", "License", "redflag package", "redflag.distributions module", "redflag.imbalance module", "redflag.importance module", "redflag.independence module", "redflag.markov module", "redflag.outliers module", "redflag.pandas module", "redflag.sklearn module", "redflag.target module", "redflag.utils module", "\ud83d\udea9 What is redflag?"], "terms": {"welcom": [0, 2], "redflag": [0, 5, 7, 9], "It": [0, 1, 5, 13, 18, 19, 21, 22], "": [0, 1, 2, 3, 5, 6, 7, 8, 10, 15, 16, 18, 19, 20, 21], "still": [0, 3, 5, 22], "earli": [0, 5], "dai": 0, "thi": [0, 1, 2, 3, 5, 6, 7, 10, 13, 14, 16, 17, 18, 19, 20, 21, 22], "librari": [0, 1, 8, 22], "ar": [0, 1, 2, 3, 5, 6, 7, 8, 10, 12, 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"0.1.0, 30 January 2022": [[5, "id3"]], "Contributing": [[6, "contributing"], [7, "contributing"]], "Code of conduct": [[6, "code-of-conduct"]], "Authorship": [[6, "authorship"]], "License": [[6, "license"], [10, "license"]], "Development": [[7, "development"]], "Installation": [[7, "installation"]], "Testing": [[7, "testing"]], "Building the package": [[7, "building-the-package"]], "Building the docs": [[7, "building-the-docs"]], "Continuous integration": [[7, "continuous-integration"]], "Redflag: safer ML by design": [[8, "redflag-safer-ml-by-design"]], "Quick start": [[8, "quick-start"]], "User guide": [[8, "user-guide"], [8, null]], "API reference": [[8, "api-reference"], [8, null]], "Other resources": [[8, "other-resources"], [8, null]], "Indices and tables": [[8, "indices-and-tables"]], "\ud83d\udea9 Installation": [[9, "installation"]], "Optional dependencies": [[9, "optional-dependencies"]], "redflag package": [[11, "redflag-package"]], "Submodules": [[11, "submodules"]], 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\ No newline at end of file
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\ No newline at end of file