diff --git a/.doctrees/_notebooks/Basic_usage.doctree b/.doctrees/_notebooks/Basic_usage.doctree index b9862e4..75dbdf4 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 4b9776a..20e3d59 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 fe55001..a2138c9 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 0b995cf..f663cbf 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 f4bb85b..77978d1 100644 Binary files a/.doctrees/environment.pickle and b/.doctrees/environment.pickle differ diff --git a/.doctrees/index.doctree b/.doctrees/index.doctree index 39842c6..bc86255 100644 Binary files a/.doctrees/index.doctree and b/.doctrees/index.doctree differ diff --git a/_images/450b80bfa0cd04cfa435ed31fc3779e5ef7129d75198face14a30712ac7fdac7.png b/_images/450b80bfa0cd04cfa435ed31fc3779e5ef7129d75198face14a30712ac7fdac7.png new file mode 100644 index 0000000..1116177 Binary files /dev/null and b/_images/450b80bfa0cd04cfa435ed31fc3779e5ef7129d75198face14a30712ac7fdac7.png differ diff --git a/_images/619414c4ee6362a53b781d50e5c574ec0322de8b96d3acf08b678a7b6b5504d6.png b/_images/619414c4ee6362a53b781d50e5c574ec0322de8b96d3acf08b678a7b6b5504d6.png deleted file mode 100644 index d7f3d3b..0000000 Binary files a/_images/619414c4ee6362a53b781d50e5c574ec0322de8b96d3acf08b678a7b6b5504d6.png and /dev/null differ diff --git a/_notebooks/Basic_usage.html b/_notebooks/Basic_usage.html index 952c00f..36e0b36 100644 --- a/_notebooks/Basic_usage.html +++ b/_notebooks/Basic_usage.html @@ -241,7 +241,7 @@

🚩 Basic usage -
'0.4.0'
+
'0.1.dev1+ga3e5e5e'
@@ -547,7 +547,7 @@

Imbalance metrics -
<matplotlib.lines.Line2D at 0x7f9d95c6a390>
+
<matplotlib.lines.Line2D at 0x7fa5a8b9ddd0>
 

 

 ../_images/2bb44d03d42247969124582eea41e70bf462d350c4bfaceb853eecf4e89f5d6d.png
@@ -630,7 +630,7 @@ 
Outliers
<seaborn.axisgrid.JointGrid at 0x7f9d5504d390>
+
<seaborn.axisgrid.JointGrid at 0x7fa58c9b2690>
 

 

 ../_images/7f1d114ebd5dc69f6ffee981278b2058bc2c78ca4b6ea7e3ad264388429d3ee5.png
@@ -689,10 +689,10 @@ 
Outliers
<seaborn.axisgrid.FacetGrid at 0x7f9d54f79690>
+
<seaborn.axisgrid.FacetGrid at 0x7fa58c978950>
 

 

-../_images/619414c4ee6362a53b781d50e5c574ec0322de8b96d3acf08b678a7b6b5504d6.png
+../_images/450b80bfa0cd04cfa435ed31fc3779e5ef7129d75198face14a30712ac7fdac7.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).

@@ -737,7 +737,7 @@ 
Clipping
<seaborn.axisgrid.FacetGrid at 0x7f9d54f48d10>
+
<seaborn.axisgrid.FacetGrid at 0x7fa58ca48950>
 

 

 ../_images/cd5838a765b85cc46ad4d3822253aaa1b0e9802d751724777bec08aee732895f.png
@@ -782,7 +782,7 @@ 
Distribution shape
-
Distribution(name='gumbel_r', shape=[], loc=10.04057253630259, scale=4.93432972751726)
+
Distribution(name='gumbel_r', shape=[], loc=10.040572536302586, scale=4.93432972751726)
 

 

 

@@ -798,7 +798,7 @@ 
Distribution shape
<seaborn.axisgrid.FacetGrid at 0x7f9d54ce9fd0>
+
<seaborn.axisgrid.FacetGrid at 0x7fa58c866d10>
 

 

 ../_images/9a24bafc24d9b00917a0e1f2bf75c12f68152f5e5322a8a58a83974f1943cf77.png
@@ -947,7 +947,7 @@ 
Feature importance
-
array([0.48750956, 0.22376372, 0.23550932, 0.05056213, 0.        ])
+
array([0.24840897, 0.34972206, 0.32817662, 0.07369235, 0.        ])
 

 

 

@@ -974,7 +974,7 @@ 
Feature importance
-
array([0, 2, 1])
+
array([1, 2, 0])
 

 

 

@@ -992,7 +992,7 @@ 
Feature importance
-
array([0.10075571, 0.36348681, 0.5105534 , 0.02520408, 0.        ])
+
array([0.08955964, 0.35788656, 0.525743  , 0.0268108 , 0.        ])
 

 

 

diff --git a/_notebooks/Tutorial.html b/_notebooks/Tutorial.html
index dd81846..cbbfd42 100644
--- a/_notebooks/Tutorial.html
+++ b/_notebooks/Tutorial.html
@@ -318,7 +318,7 @@ 
A quick look at 
 

 

-
'0.4.0'
+
'0.1.dev1+ga3e5e5e'
 

 

 

@@ -570,7 +570,7 @@ 
Clipping
<seaborn.axisgrid.FacetGrid at 0x7f55b5cbd610>
+
<seaborn.axisgrid.FacetGrid at 0x7f4a91901850>
 

 

 ../_images/cd5838a765b85cc46ad4d3822253aaa1b0e9802d751724777bec08aee732895f.png
@@ -623,7 +623,7 @@ 
Importance
-
array([0.43341464, 0.16440126, 0.31468974, 0.08749436])
+
array([0.40817216, 0.20385381, 0.31665051, 0.07132352])
 

 

 

@@ -733,7 +733,7 @@ 
Pipelines
🚩 Feature 3 has low importance; check for relevance.
-ℹ️ Dummy classifier scores: {'f1': 0.25488459423559595, 'roc_auc': 0.5} (most_frequent strategy).
+ℹ️ Dummy classifier scores: {'f1': 0.2562046792503389, 'roc_auc': 0.4937139556627474} (stratified strategy).
 

 

 

 
Pipeline(steps=[('detector',
-                 Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7f55b5bd7c40>,
+                 Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7f4a917bfce0>,
                           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 2e2510c..4ab23f2 100644
--- a/_notebooks/Using_redflag_with_Pandas.html
+++ b/_notebooks/Using_redflag_with_Pandas.html
@@ -234,7 +234,7 @@ 
🚩 Using redf
 

 

 

-
'0.4.0'
+
'0.1.dev1+ga3e5e5e'
 

 

 

@@ -430,8 +430,8 @@ 
Series accessor
-
{'f1': 0.25351640471373643,
- 'roc_auc': 0.494193275325803,
+
{'f1': 0.24308613668344808,
+ 'roc_auc': 0.49544118310710333,
  'strategy': 'stratified',
  'task': 'classification'}
 

@@ -463,9 +463,9 @@ 
Series accessor
 
Continuous data suitable for regression
-Outliers:    [  34   35  136  140  141  142  143  145  147  175  180  181  182  532
-  583  633  662  768  769  801 1316 1547 1731 1732 1744 1754 1756 1778
- 1779 1780 1788 2884 2932 2973 2974 3004 3079 3080 3087 3109]
+Outliers:    [  34   35  140  141  142  143  175  182  532  581  583  633  662  757
+  768  769  801 1316 1547 1744 1754 1756 1778 1779 1780 1784 1788 1808
+ 1812 2884 2932 2973 2974 3004 3079 3080 3087 3094 3109]
 Correlated:  True
 Dummy scores:{'mean': {'mean_squared_error': 47528.78263092096, 'r2': 0.0}}
 

diff --git a/_notebooks/Using_redflag_with_sklearn.html b/_notebooks/Using_redflag_with_sklearn.html
index a859b19..0148a34 100644
--- a/_notebooks/Using_redflag_with_sklearn.html
+++ b/_notebooks/Using_redflag_with_sklearn.html
@@ -574,7 +574,7 @@ 
Using the pre-built 
🚩 There are more outliers than expected in the training data (349 vs 31).
 

 

-
ℹ️ Dummy classifier scores: {'f1': 0.2640071145283919, 'roc_auc': 0.5004869752654918} (stratified strategy).
+
ℹ️ Dummy classifier scores: {'f1': 0.2553305717063476, 'roc_auc': 0.5040393210009199} (stratified strategy).
 

 

 
Pipeline(steps=[('standardscaler', StandardScaler()),
@@ -743,7 +743,7 @@ 
The imbalance comparator
 
🚩 There is a different number of minority classes (2) compared to the training data (4).
-🚩 The minority classes (dolomite, sandstone) are different from those in the training data (wackestone, dolomite, mudstone, sandstone).
+🚩 The minority classes (sandstone, dolomite) are different from those in the training data (wackestone, sandstone, dolomite, mudstone).
 

 

 
array([[  66.276     , 2359.73324716,    3.591     ],
@@ -779,12 +779,12 @@ 
Making your own smoke detector
 
Pipeline(steps=[('detector',
-                 Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7ff170f47560>,
+                 Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7f9497cdb380>,
                           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.

@@ -811,30 +811,30 @@ 
Making your own smoke detector
Pipeline(steps=[('standardscaler', StandardScaler()),
                 ('pipeline',
                  Pipeline(steps=[('detector-1',
-                                  Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7ff170f472e0>,
+                                  Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7f9497cdb6a0>,
                                            message='fail custom func '
                                                    'has_nans()')),
                                  ('detector-2',
-                                  Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7ff170f47740>,
+                                  Detector(func=<function BaseRedflagDetector.__init__.<locals>.<lambda> at 0x7f9497cdbe20>,
                                            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/_sources/index.rst.txt b/_sources/index.rst.txt
index 34829b6..52a12b3 100644
--- a/_sources/index.rst.txt
+++ b/_sources/index.rst.txt
@@ -6,14 +6,15 @@
    `Fork on GitHub `_
 
 
-Redflag: An Entrance Exam for Data
-==================================
-
-    | ``redflag`` aims to be an automatic safety net for
-    | machine learning datasets. Given a ``DataFrame`` or
-    | ``ndarray``, ``redflag`` will analyse each feature,
-    | including aspects such as class imbalance, leakage, outliers,
-    | anomalous data patterns, threats to the IID assumption, etc.
+Redflag: safer ML by design
+===========================
+
+    | ``redflag`` is a lightweight safety net for machine
+    | learning. Given a ``DataFrame`` or ``ndarray``,
+    | ``redflag`` will analyse the features and the target,
+    | and warn you about class imbalance, leakage, outliers,
+    | anomalous data patterns, threats to the IID assumption,
+    | and more.
 
 
 Quick start
diff --git a/_static/agile-open-logo-nocircle-grey_40px.png b/_static/agile-open-logo-nocircle-grey_40px.png
deleted file mode 100644
index cab8c74..0000000
Binary files a/_static/agile-open-logo-nocircle-grey_40px.png and /dev/null differ
diff --git a/_static/redflag_social.svg b/_static/redflag_social.svg
index 70e4727..b6ca7a6 100644
--- a/_static/redflag_social.svg
+++ b/_static/redflag_social.svg
@@ -2,19 +2,20 @@
 
 
 redflagredflagmachine  learning  safety
+         style="font-style:normal;font-variant:normal;font-weight:normal;font-stretch:normal;font-size:11.4206px;font-family:'Mercury Text G4';-inkscape-font-specification:'Mercury Text G4, Normal';font-variant-ligatures:normal;font-variant-caps:normal;font-variant-numeric:normal;font-variant-east-asian:normal;fill:#1866ac;fill-opacity:1;stroke-width:0.80938"
+         x="184.72946"
+         y="32.095646">a safety net for yourmachine learningdata & pipelinesscienxlab.org/redflag
diff --git a/index.html b/index.html
index 05f8da9..bf21931 100644
--- a/index.html
+++ b/index.html
@@ -223,15 +223,16 @@
           
-

-
Redflag: An Entrance Exam for Data#

+

+
Redflag: safer ML by design#

 

 

-
redflag aims to be an automatic safety net for

-
machine learning datasets. Given a DataFrame or

-
ndarray, redflag will analyse each feature,

-
including aspects such as class imbalance, leakage, outliers,

-
anomalous data patterns, threats to the IID assumption, etc.

+
redflag is a lightweight safety net for machine

+
learning. Given a DataFrame or ndarray,

+
redflag will analyse the features and the target,

+
and warn you about class imbalance, leakage, outliers,

+
anomalous data patterns, threats to the IID assumption,

+
and more.

 

 

 

diff --git a/installation.html b/installation.html
index b3440e9..8794d99 100644
--- a/installation.html
+++ b/installation.html
@@ -3,7 +3,7 @@
   
     
     
-
+
 
     
         🚩 Installation - redflag documentation
diff --git a/objects.inv b/objects.inv
index d37d202..5e7071c 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/searchindex.js b/searchindex.js
index 39b1634..1bf46b7 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file