From 15cc97f36fdbb565fab397d40b8f47cc0dceca06 Mon Sep 17 00:00:00 2001 From: Shivendra Sharma Date: Thu, 17 Oct 2019 20:24:53 +0530 Subject: [PATCH 01/44] Corrected typo (#981) Fixed small typo in the Learning Curve documentation. --- docs/api/model_selection/learning_curve.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/api/model_selection/learning_curve.rst b/docs/api/model_selection/learning_curve.rst index a5da18aff..940e6ed17 100644 --- a/docs/api/model_selection/learning_curve.rst +++ b/docs/api/model_selection/learning_curve.rst @@ -3,7 +3,7 @@ Learning Curve ============== -A learning curve shows the relationship of the training score versus the cross validated test score for an estimator with a varying number of training samples. This visualization is typically used two show two things: +A learning curve shows the relationship of the training score versus the cross validated test score for an estimator with a varying number of training samples. This visualization is typically used to show two things: 1. How much the estimator benefits from more data (e.g. do we have "enough data" or will the estimator get better if used in an online fashion). 2. If the estimator is more sensitive to error due to variance vs. error due to bias. From a509d63e8b05f66150afcfa9216b2cbd9b01ce9c Mon Sep 17 00:00:00 2001 From: mchestnut91 <40185521+mchestnut91@users.noreply.github.com> Date: Sat, 19 Oct 2019 16:46:52 -0400 Subject: [PATCH 02/44] Changed rocauc quick method to use show, add image comparison test (#977) * added rocauc quickmethod and image comparison test * added rocauc quickmethod and image comparison test * added documentation for rocauc quick method --- docs/api/classifier/rocauc.rst | 31 ++++++++++- .../test_rocauc/test_rocauc_quickmethod.png | Bin 0 -> 19954 bytes tests/test_classifier/test_rocauc.py | 8 +-- yellowbrick/classifier/rocauc.py | 52 ++++++++++++------ 4 files changed, 70 insertions(+), 21 deletions(-) create mode 100644 tests/baseline_images/test_classifier/test_rocauc/test_rocauc_quickmethod.png diff --git a/docs/api/classifier/rocauc.rst b/docs/api/classifier/rocauc.rst index 9419c9cb3..3bb0d5642 100644 --- a/docs/api/classifier/rocauc.rst +++ b/docs/api/classifier/rocauc.rst @@ -9,6 +9,13 @@ The Receiver Operating Characteristic (ROC) is a measure of a classifier's predi This leads to another metric, area under the curve (AUC), which is a computation of the relationship between false positives and true positives. The higher the AUC, the better the model generally is. However, it is also important to inspect the "steepness" of the curve, as this describes the maximization of the true positive rate while minimizing the false positive rate. +================= ================= +Visualizer `ROCAUC `_ +Quick Method `roc_auc() `_ +Models Classification +Workflow Model evaluation +================= ================= + .. plot:: :context: close-figs :alt: ROCAUC Binary Classification @@ -82,11 +89,33 @@ ROC curves are typically used in binary classification, and in fact the Scikit-L By default with multi-class ROCAUC visualizations, a curve for each class is plotted, in addition to the micro- and macro-average curves for each class. This enables the user to inspect the tradeoff between sensitivity and specificity on a per-class basis. Note that for multi-class ``ROCAUC``, at least one of the ``micro``, ``macro``, or ``per_class`` parameters must be set to ``True`` (by default, all are set to ``True``). +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method ``roc_auc``. This method will build the ``ROCAUC`` object with the associated arguments, fit it, then (optionally) immediately show it + +.. plot:: + :context: close-figs + :alt: roc_auc on the occupancy dataset + + from yellowbrick.classifier.rocauc import roc_auc + from yellowbrick.datasets import load_credit + from sklearn.linear_model import LogisticRegression + from sklearn.model_selection import train_test_split + + #Load the classification dataset + X, y = load_credit() + + #Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X,y) + + # Instantiate the visualizer with the classification model + model = LogisticRegression() + roc_auc(model, X_train, y_train, X_test=X_test, y_test=y_test, classes=['not_defaulted', 'defaulted']) API Reference ------------- .. automodule:: yellowbrick.classifier.rocauc - :members: ROCAUC + :members: ROCAUC, roc_auc :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_classifier/test_rocauc/test_rocauc_quickmethod.png b/tests/baseline_images/test_classifier/test_rocauc/test_rocauc_quickmethod.png new file mode 100644 index 0000000000000000000000000000000000000000..430255f80f564f0aeb6079d544c6fb13f9cb5b12 GIT binary patch literal 19954 zcmeHvWmHvb+x4Om6%;{GLAI3As309G2uOD$f{4Th>4t5fgoGer5Yk=Jtw<}~snRXo z@LkL2dB^+n`~QvajB^I#xEE`$`@XN3b6#`luc9P#n(QJOf*_~mWTn&)gaCse_^hPF z@F$r@%U|IKp~D?H4N~~Wlk|}v{QrrkvbqikLh%s&4=+t3)dK!d$WdCyQQh{jqsv2k zGX(q4@u{_~qqT(z#@Wo?!NS%CbCa8w`vxcGiKF9FVIH3Uy@1=+-ke7eCz6dI7(`C$ zwubAc#Zfm`jj6-q<9;FY$i>hzk{C>&-}8ic#*5PFYIf<_oI~X+CyO<9HLbG$*w7RW zsmWVyc2DVB@la>>vY(fhN_bv`(|G&mIU|OQ`O(Kmkq+KI3ek^{+sr@8%%^utxw)rD zZiZKehrcg-FjKwaz}dboiU&)OBp&dlfu9J*=Zqx!^U*0hLii!s@c;k!|B>BLs9nMD zuvz_6zb2~u)wp8?;vLDO&(~3iG_&~bch);9tcVmtyeg_3JHvJ{$Y%Sfw4bp*(7co# zL2w6=vZa$1GG8hjTukB<&Db#$Vg$5S^~ERH-vme%?U zxnv?a<-)Eu7g+no;PW@mYZ?v6uS#hs=9d@mSM`{dShucE4qTyr-9qIuQNShBDJ7M? zniAtV!}XwXc8gmMk&m5hRg-ud*CD|l>J=bd}6#(C-9no6hF?YiT%J+F(FR%kTM zxH9Go4qwpfdm7O6eb{?y>7gPmC3fFL#Ivm>$ak-j?cLDjzC=|+GD`T90_8x>@u8#Z zHw#=|ki8HYiwo>~m%%@F^wnrIS7}S>sBb!VK7ngR=t^VnY&CqGZ6}|byBs#eeY%mq zz-59w?n_yH=O3r!^reeI@pPsFw z(T{~L{gFMz3(3bfI9t0#;;0uU%C~Bsovm8vO%yDXs3LEdtF{pvjNBOCoNG@Xt57og z?Ktm1w@szR}{*p05&Q|YcMrC{4QiPrOWJ8LT(; z7S#LLmBVIqEuScg%Y<3Cs@q-rtUO<**MG()Q)~XgSVGIU$!a;*$PWp4^&^ujdCZS8 zdC&9@y<+4qe8sF=z?p8Vn@IMexnFOjh}xfMO1GJTdy-k>jO=W817%SILBY^ibOnuV zD!BW=d>X*Op_F1qdbGLa<>;8BB%+?hKv8$$e!T2rXR`Ks@7x^ zVcJOTA7$krZ;<>O_YF^;sBL;u*;?*IiDc~ap*6iz13wFx>qohpS>+2NRBA0{55*0r z8-w;%^0uiiHu)r+fUgD6?@u8-M#yjWnun?n;uwI^|BHhUB<>W)EycZc(M+zqdj#!@KF{kr) zO2?YFh9~E#UmYH@OPyVxjtwZo)4hL<6032%))3QmE@EscVf-cD|u6LPmjEF_Y{4|Hn#O}$vqN3I92iIZL$5DmL3x0 z@dCewQ)v(yLDyc4Eiy0MslW+S^BPwbL<$ZD}?smsnTP0n(Hi@5DEjzPpYe9tM!dOKg=n};Wt zy?_4a5(DW3iLHwCLl2qz^xi)DN5}mh5g9>+%KD;CHSJX7r9*o$Sox*ct72agDiZv+ zlscR5iYsTD$Lg1HXma5_od^y&Tq0?Deg;ScioCdjPx1$ zQiGQ8k{(3)u?LvYS08EZH-&52a;sqF_gCB`-M$38@ShxC)v9^TRg^tu5I?Qs5j{K3 zpAbLcyi-1W#O0}>9bYK~7q#D0SWU5i}+~=R+kMtw)i@ApH zA1w>;(XV)ll>W_R=Uel`AFvr$kO}7u`PAvh8W&Yv&MvOGQgN75oEuY}ts(QpbU1Qo$X5P% zzw)-2{K=j#g?USE&ACwr5{C=cqt#W7snw0-mwDuDee_aQ582I1#QKV}dL-n-u=?ra zHOa-p&cj}j$sr3L_|o*#pDWqNu`qO3+ZKC{2S4zU7n{R2f4HbOF|VmLpmo9B$vQF6#{q z(5ZYYDq>-$A~fig#|MpAqd>Wh6yXR1M|YUt8Mn%Zvu<}PyS5_Xgt zTCuV!$tqNO&LVG{o!RRrPEAI#*t4EQ9uu7{0%1)kM=xxkFRWDYw=y`y%LSN zm3L6}skMBtnM)~lE^&x;$g;J^WEkP{h9qn9_egXB79%4 zlama2zv80LT07bF?}z6;c|Uy3 zS4G^uu0eImTK(8rr2fIqVNF`Yl0;uf+XDxOFOfVCsz(X#H;FB-jQnwnef;x7aviyd zg2zggOU9^^;b3ug$(Xx*q}f?XX9D%SQ+(@1+Z(CJSz)Gw?pB7`M>X@k7l#tP?te{e zb+=UX(orXWko^=hm~U8+qY)cLw>COL+0!7avT*6{hnft=%}Vay(Zt%fIDYHdTPtsJ z;gy9M^xr3XkrJI4^mD13^LcS5Zi%h)R%Ps8&ZVq#hb=NwB9%2vsaV?IK9}T6=AL*J zmQ~%of*szqe8Xx>LpUr#7n>AaaY+8&>}m-|K=F8I_>c%!oVAUXps`h4iyG15qg1|* zzT2k-Bgb}J6N4AW4T)}>4<~wP?Wx|7*AL!eImX)fvs21HG48+9+uN7nVJ1NR@(y;K zX}3yza3tv&bu#-YVGEgG*7E6zmik8}%>4HzhB6Be{c^g0?_+bhmdi7)k#W%FQQOn` z(&_u&mVtk7H%`lrUJhN1|6X`}^kOz1!F?8O?@o>~%FzAtIK8}FPg!j9-D0uo53Xa= z?X!}nx<5R1TKXd2J@iVW_;rZu9rvTENsZ7so@LmRuZ2wO`Wz=n__EBkH6vg07butX zbdE|6pB>{Gj^i(W@^t$Kk+uHqMy=XnDo1N+lM_z1BKTwZ=N6KL>+g>$dDj>ZoAzvN zE-g8Kcu~ydVeSxZPbc#OL3jhrbT0}%jc1+PtUw8B$HcnoHS+-o2an>TxAE09%8bK1ZIT zp6fsNhR2UmE{nO#-j9vgAd%R4)*dl+i}lNeAM2u370a82#R)1=Jg$6znX9w5$9*28 z#yEe7vOsW@Dy9l5dk`64{VV&BkE#`c&8PxeyEM;kRo#h~ilpSF7%n%;~! 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This is particularly useful for determining cluster imbalance, or for selecting a value for :math:`K` by comparing multiple visualizers. +================= ============================== +Visualizer `SilhouetteVisualizer `_ +Quick Method `silhouette_visualizer() `_ +Models Clustering +Workflow Model evaluation +================= ============================== + +Examples and demo + .. plot:: :context: close-figs :alt: SilhouetteVisualizer on the nfl dataset with 4 clusters @@ -30,11 +39,30 @@ The Silhouette Visualizer displays the silhouette coefficient for each sample on visualizer.fit(X) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure +The same functionality above can be achieved with the associated quick method `silhouette_visualizer`. This method will build the Silhouette Visualizer object with the associated arguments, fit it, then (optionally) immediately show it. + +.. plot:: + :context: close-figs + :alt: silhouette_visualizer on the nfl dataset with 4 clusters + + from sklearn.cluster import KMeans + + from yellowbrick.cluster import silhouette_visualizer + from yellowbrick.datasets import load_credit + + # Load a clustering dataset + X, y = load_credit() + + # Specify rows to cluster: under 40 y/o and have either graduate or university education + X = X[(X['age'] <= 40) & (X['edu'].isin([1,2]))] + + # Use the quick method and immediately show the figure + silhouette_visualizer(KMeans(5, random_state=42), X, colors='yellowbrick') API Reference ------------- .. automodule:: yellowbrick.cluster.silhouette - :members: SilhouetteVisualizer + :members: SilhouetteVisualizer, silhouette_visualizer :undoc-members: :show-inheritance: diff --git a/tests/test_cluster/test_silhouette.py b/tests/test_cluster/test_silhouette.py index 4df85b7d2..b47d84043 100644 --- a/tests/test_cluster/test_silhouette.py +++ b/tests/test_cluster/test_silhouette.py @@ -183,7 +183,7 @@ def test_quick_method(self): ) model = MiniBatchKMeans(3, random_state=343) - oz = silhouette_visualizer(model, X, random_state=93, legend=False) + oz = silhouette_visualizer(model, X, show=False) assert isinstance(oz, SilhouetteVisualizer) self.assert_images_similar(oz) diff --git a/yellowbrick/cluster/silhouette.py b/yellowbrick/cluster/silhouette.py index ae956ea8c..5a917d413 100644 --- a/yellowbrick/cluster/silhouette.py +++ b/yellowbrick/cluster/silhouette.py @@ -73,10 +73,10 @@ class SilhouetteVisualizer(ClusteringScoreVisualizer): Yellowbrick or matplotlib colormap string. is_fitted : bool or str, default='auto' - Specify if the wrapped estimator is already fitted. If False, the estimator - will be fit when the visualizer is fit, otherwise, the estimator will not be - modified. If 'auto' (default), a helper method will check if the estimator - is fitted before fitting it again. + Specify if the wrapped estimator is already fitted. If False, the + estimator will be fit when the visualizer is fit, otherwise, the + estimator will not be modified. If 'auto' (default), a helper method + will check if the estimator is fitted before fitting it again. kwargs : dict Keyword arguments that are passed to the base class and may influence @@ -265,7 +265,7 @@ def finalize(self): def silhouette_visualizer( - model, X, y=None, ax=None, colors=None, is_fitted="auto", **kwargs + model, X, y=None, ax=None, colors=None, is_fitted="auto", show=True, **kwargs ): """Quick Method: The Silhouette Visualizer displays the silhouette coefficient for each @@ -285,26 +285,31 @@ def silhouette_visualizer( or ``MiniBatchKMeans``). If the estimator is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by ``is_fitted``. - ax : matplotlib Axes, default: None - The axes to plot the figure on. If None is passed in the current axes - will be used (or generated if required). - X : array-like of shape (n, m) A matrix or data frame with n instances and m features y : array-like of shape (n,), optional A vector or series representing the target for each instance + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). + colors : iterable or string, default: None A collection of colors to use for each cluster group. If there are fewer colors than cluster groups, colors will repeat. May also be a Yellowbrick or matplotlib colormap string. is_fitted : bool or str, default='auto' - Specify if the wrapped estimator is already fitted. If False, the estimator - will be fit when the visualizer is fit, otherwise, the estimator will not be - modified. If 'auto' (default), a helper method will check if the estimator - is fitted before fitting it again. + Specify if the wrapped estimator is already fitted. If False, the + estimator will be fit when the visualizer is fit, otherwise, the + estimator will not be modified. If 'auto' (default), a helper method + will check if the estimator is fitted before fitting it again. + + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however + you cannot call ``plt.savefig`` from this signature, nor + ``clear_figure``. If False, simply calls ``finalize()`` kwargs : dict Keyword arguments that are passed to the base class and may influence @@ -315,10 +320,15 @@ def silhouette_visualizer( viz : SilhouetteVisualizer The silhouette visualizer, fitted and finalized. """ + oz = SilhouetteVisualizer( model, ax=ax, colors=colors, is_fitted=is_fitted, **kwargs ) - oz.fit(X, y) - oz.finalize() + + if show: + oz.show() + else: + oz.finalize() + return oz From d33030364db5b010fb55b93bebbc6f465172c291 Mon Sep 17 00:00:00 2001 From: naresh-bachwani <43993586+naresh-bachwani@users.noreply.github.com> Date: Sun, 10 Nov 2019 00:42:22 +0530 Subject: [PATCH 04/44] RadViz quick methods updates (#990) * Quick method, tests and Documentation * Updated docs * Made the neccessary changes --- docs/api/features/radviz.rst | 31 +++- .../test_radviz/test_radviz_quick_method.png | Bin 0 -> 24572 bytes tests/test_features/test_radviz.py | 9 + yellowbrick/features/radviz.py | 164 ++++++++++-------- 4 files changed, 128 insertions(+), 76 deletions(-) create mode 100644 tests/baseline_images/test_features/test_radviz/test_radviz_quick_method.png diff --git a/docs/api/features/radviz.rst b/docs/api/features/radviz.rst index 326931025..1a2a31c0d 100644 --- a/docs/api/features/radviz.rst +++ b/docs/api/features/radviz.rst @@ -22,6 +22,13 @@ percent missing. If you do receive this warning, you may want to look at imputation strategies. A good starting place is the `scikit-learn Imputer. `_ +================= ================= +Visualizer `RadViz `_ +Quick Method `radviz() `_ +Models Classification, Regression +Workflow Feature Engineering +================= ================= + .. plot:: :context: close-figs :alt: RadViz on the Occupancy Dataset @@ -45,10 +52,32 @@ A good starting place is the `scikit-learn Imputer. f9fF8+Uh7jyG)5U*W` zVnGld1at9%pr8C@B~ln-QbzS1>ys@v!Sj9$JCk1hvnQ~=^_%|r0>h08uA31CXxtn;a9{r2Fr6NJU zQ2oxRihg`|=>Pxt{}F27o{Ayo|4g*la3yH*YsN`fp;EB>+Giny<8ajP&rfdFr{FXY z3=jOwmC)gJ0)C|qbFR(h4-;Tq>g*^tdr5CX3SWjE=2E;}{{W}OKn18>0*NeCD+!*1DBqN(I9dBFf=27R#sMqymx8Hc7INW7zM(l zSxxjz=U11qB6;@1l?B=uW*9M_9j5^B@TPURzgJ*3@L>;^N}u?A)+FKR#}4)!=g( z$glqQ_IJ1DTlW9F?D6{b@kl(ElDWOTJ*5a4;)z{qkVAOxxYk)zR8%;*x;F3I zXlZq~3vsv8jYKR=<3i`s7JTTwL6IF5huQt^`Z5^nieXfp6dZmB_PH zLiYn$J>FOjbE`NFoMt|LytKGj`~Ca(!e`D5L?7Y5VQhm_-XmTM-e{O;+MjpG2{y(O zTgez^Sz0zDN~)38(@}!GTKdrq_o*0ku(!H1>|Zk>pGwQhbmQaWht)sKt9wqzRNr8k$>8_zb91zCi1y0HfgG;LfhD{5CG+ 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z1D$+#dy9g|7n@iNnsyBN^*#IC_C~6xi)H^Fyp|uQetKADEhN;Eaf}l=+2P_{N((Cb z?djB-QMs*g(H-y0N!`R`3|7~NYbLI5tzu_0d6COZy6NbQYr2qM(KVlf6<9{0-B!tLZ&oO_W1c}GvXq&{iB<8cd@fNZLgU9+TcSK+DFz(~G zEGYTGmBhUG#k>7=5+UPGkC>K;&omtzzHksC*@Se7CU3iYJT;w}jm_Hu@tO|%>*L<{ zRdA>Ju6Q}2njG_?*4xERe9Tn%SY;zeCd6ZWHh+OMtoS?Cognik|EW)c+MZinJQJtK zI}iF6+_YP~p Date: Wed, 13 Nov 2019 08:35:20 +0530 Subject: [PATCH 05/44] CVScores quick method update (#994) * updated cvscore quick-method * minor changes * fixed issues with rendering visualizations in cross_validation.rst and moved visualizer description to the top --- docs/api/model_selection/cross_validation.rst | 35 ++++++++++++++++++- .../test_cross_validation.py | 2 +- .../model_selection/cross_validation.py | 16 +++++++-- 3 files changed, 49 insertions(+), 4 deletions(-) diff --git a/docs/api/model_selection/cross_validation.rst b/docs/api/model_selection/cross_validation.rst index 1610688a0..ee60a29ad 100644 --- a/docs/api/model_selection/cross_validation.rst +++ b/docs/api/model_selection/cross_validation.rst @@ -3,6 +3,13 @@ Cross Validation Scores ======================= +================= ===================== + Visualizer `CVScores `_ + Quick Method `cv_scores() `_ + Models Classification, Regression + Workflow Model Selection +================= ===================== + Generally we determine whether a given model is optimal by looking at it's F1, precision, recall, and accuracy (for classification), or it's coefficient of determination (R2) and error (for regression). However, real world data is often distributed somewhat unevenly, meaning that the fitted model is likely to perform better on some sections of the data than on others. Yellowbrick's ``CVScores`` visualizer enables us to visually explore these variations in performance using different cross validation strategies. Cross Validation @@ -14,6 +21,7 @@ Cross-validation starts by shuffling the data (to prevent any unintentional orde In Yellowbrick, the ``CVScores`` visualizer displays cross-validated scores as a bar chart (one bar for each fold) with the average score across all folds plotted as a horizontal dotted line. + Classification -------------- @@ -74,10 +82,35 @@ In this next example we show how to visualize cross-validated scores for a regre As with our classification ``CVScores`` visualization, our regression visualization suggests that our ``Ridge`` regressor performs very well (e.g. produces a high coefficient of determination) across nearly every fold, resulting in another fairly high overall R2 score. + +Quick Method +------------ + The same functionality above can be achieved with the associated quick method ``cv_scores``. This method will build the ``CVScores`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. + +.. plot:: + :context: close-figs + :alt: Cross validation on the energy data set using KFold + + from sklearn.linear_model import Ridge + from sklearn.model_selection import KFold + + from yellowbrick.datasets import load_energy + from yellowbrick.model_selection import cv_scores + + # Load the regression dataset + X, y = load_energy() + + # Instantiate the regression model and visualizer + cv = KFold(n_splits=12, random_state=42) + + model = Ridge() + visualizer = cv_scores(model, X, y, cv=cv, scoring='r2') + + API Reference ------------- .. automodule:: yellowbrick.model_selection.cross_validation - :members: CVScores + :members: CVScores, cv_scores :undoc-members: :show-inheritance: diff --git a/tests/test_model_selection/test_cross_validation.py b/tests/test_model_selection/test_cross_validation.py index bc5df7a13..a3582c5f5 100644 --- a/tests/test_model_selection/test_cross_validation.py +++ b/tests/test_model_selection/test_cross_validation.py @@ -147,7 +147,7 @@ def test_quick_method(self): X, y = self.classification cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=321) - viz = cv_scores(SVC(), X, y, cv=cv) + viz = cv_scores(SVC(), X, y, cv=cv, show=False) self.assert_images_similar(viz, tol=2.0) diff --git a/yellowbrick/model_selection/cross_validation.py b/yellowbrick/model_selection/cross_validation.py index f471058b8..287f815b1 100644 --- a/yellowbrick/model_selection/cross_validation.py +++ b/yellowbrick/model_selection/cross_validation.py @@ -192,7 +192,9 @@ def finalize(self, **kwargs): ########################################################################## -def cv_scores(model, X, y, ax=None, cv=None, scoring=None, color=None, **kwargs): +def cv_scores( + model, X, y, ax=None, cv=None, scoring=None, color=None, show=True, **kwargs +): """ Displays cross validation scores as a bar chart and the average of the scores as a horizontal line @@ -243,6 +245,11 @@ def cv_scores(model, X, y, ax=None, cv=None, scoring=None, color=None, **kwargs) color: string Specify color for barchart + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -259,6 +266,11 @@ def cv_scores(model, X, y, ax=None, cv=None, scoring=None, color=None, **kwargs) # Fit and show the visualizer visualizer.fit(X, y) - visualizer.show(**kwargs) + if show: + visualizer.show() + else: + visualizer.finalize() + + # Return the visualizer object return visualizer From 6358711204798f94a9c242f73a6c9dd03dd88f39 Mon Sep 17 00:00:00 2001 From: Daniel Navarrete Date: Sun, 24 Nov 2019 15:44:21 -0500 Subject: [PATCH 06/44] Update hyperlinks in repo documentation (.md) files. (#998) --- CONTRIBUTING.md | 10 +++++----- DESCRIPTION.md | 3 +-- MAINTAINERS.md | 2 +- README.md | 12 ++++++------ 4 files changed, 13 insertions(+), 14 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 4611e3ca6..097ffe044 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,6 +1,6 @@ # Contributing to Yellowbrick -**NOTE: This document is a "getting started" summary for contributing to the Yellowbrick project.** To read the full contributor's guide, please visit the [contributing page](http://www.scikit-yb.org/en/latest/about.html#contributing) in the documentation. Please make sure to read this page carefully to ensure the review process is as smooth as possible and to ensure the greatest likelihood of having your contribution be merged. +**NOTE: This document is a "getting started" summary for contributing to the Yellowbrick project.** To read the full contributor's guide, please visit the [contributing page](http://www.scikit-yb.org/en/latest/contributing/index.html) in the documentation. Please make sure to read this page carefully to ensure the review process is as smooth as possible and to ensure the greatest likelihood of having your contribution be merged. For more on the development path, goals, and motivations behind Yellowbrick, check out our developer presentation: [Visualizing Model Selection with Scikit-Yellowbrick: An Introduction to Developing Visualizers](http://www.slideshare.net/BenjaminBengfort/visualizing-model-selection-with-scikityellowbrick-an-introduction-to-developing-visualizers). @@ -16,11 +16,11 @@ Beyond creating visualizers, there are many ways to contribute: - Contribute a Jupyter notebook to our examples[ gallery](https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples). - Assist us with [user testing](http://www.scikit-yb.org/en/latest/evaluation.html). - Add to the documentation or help with our website, [scikit-yb.org](http://www.scikit-yb.org). -- Write unit or integration tests for our project. +- Write [unit or integration tests](https://www.scikit-yb.org/en/latest/contributing/developing_visualizers.html#integration-tests) for our project. - Answer questions on our issues, mailing list, Stack Overflow, and elsewhere. - Translate our documentation into another language. - Write a blog post, tweet, or share our project with others. -- Teach someone how to use Yellowbrick. +- [Teach](https://www.scikit-yb.org/en/latest/teaching.html) someone how to use Yellowbrick. As you can see, there are lots of ways to get involved and we would be very happy for you to join us! The only thing we ask is that you abide by the principles of openness, respect, and consideration of others as described in the [Python Software Foundation Code of Conduct](https://www.python.org/psf/codeofconduct/). @@ -250,7 +250,7 @@ $ pytest tests/test_your_visualizer.py The Makefile uses the pytest runner and testing suite as well as the coverage library, so make sure you have those dependencies installed! -**Note**: Advanced developers can use our _image comparison tests_ to assert that an image generated matches a baseline image. Read more about this in our [testing documentation](http://www.scikit-yb.org/en/latest/contributing.html#testing) +**Note**: Advanced developers can use our _image comparison tests_ to assert that an image generated matches a baseline image. Read more about this in our [testing documentation](https://www.scikit-yb.org/en/latest/contributing/developing_visualizers.html#image-comparison-tests). ### Documentation @@ -296,4 +296,4 @@ class MyVisualizer(Visualizer): """ ``` -This is a very good start to producing a high quality visualizer, but unless it is part of the documentation on our website, it will not be visible. For details on including documentation in the `docs` directory see the [Contributing Documentation](http://www.scikit-yb.org/en/latest/contributing.html#documentation) section in the larger contributing guide. \ No newline at end of file +This is a very good start to producing a high quality visualizer, but unless it is part of the documentation on our website, it will not be visible. For details on including documentation in the `docs` directory see the [Contributing Documentation](https://www.scikit-yb.org/en/latest/contributing/index.html) section in the larger contributing guide. diff --git a/DESCRIPTION.md b/DESCRIPTION.md index fb96a2e30..e2f3c8fd2 100644 --- a/DESCRIPTION.md +++ b/DESCRIPTION.md @@ -63,8 +63,7 @@ Visualizers are estimators — objects that learn from data — whose pr - **t-SNE Corpus Visualization**: uses stochastic neighbor embedding to project documents - **UMAP Corpus Visualization**: plot similar documents closer together to discover clusters -... and more! Yellowbrick is adding new visualizers all the time so be sure to check out our [examples gallary](https://www.scikit-yb.org/en/latest/api/index.html) — or even the [develop](https://github.com/districtdatalabs/yellowbrick/tree/develop) branch — and feel free to contribute your ideas for new Visualizers! +... and more! Yellowbrick is adding new visualizers all the time so be sure to check out our [examples gallery]https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples) — or even the [develop](https://github.com/districtdatalabs/yellowbrick/tree/develop) branch — and feel free to contribute your ideas for new Visualizers! ## Affiliations [![District Data Labs](https://github.com/DistrictDataLabs/yellowbrick/raw/develop/docs/images/readme/affiliates_ddl.png)](https://www.districtdatalabs.com/) [![NumFOCUS Affiliated Project](https://github.com/DistrictDataLabs/yellowbrick/raw/develop/docs/images/readme/affiliates_numfocus.png)](https://numfocus.org/) - diff --git a/MAINTAINERS.md b/MAINTAINERS.md index 5e001317c..a482cc55b 100644 --- a/MAINTAINERS.md +++ b/MAINTAINERS.md @@ -4,7 +4,7 @@ This file describes how the Yellowbrick project is maintained and provides conta When creating a pull request, your contribution will be reviewed by one or probably two maintainers who will give you the :+1: when your extension is ready to be merged. Maintainers work hard to ensure that Yellowbrick is a high quality project and that contributors are successful. -For more about how to develop visualizers and contribute features to Yellowbrick, see our [contributor's guide](CONTRIBUTING.md) and the documentation. +For more about how to develop visualizers and contribute features to Yellowbrick, see our [contributor's guide](CONTRIBUTING.md) and the [documentation](https://www.scikit-yb.org/en/latest/contributing/index.html). For everyone who has [contributed](https://github.com/DistrictDataLabs/yellowbrick/graphs/contributors) in big and in small ways, **thank you!**. Yellowbrick is intended to be a community project, welcoming to new and experienced developers alike. If you would like to become a core contributor you must simply submit a pull request that shows core knowledge of the Yellowbrick library. Usually new Visualizers meet this standard; let the maintainers know you'd like to join the team, and they'll help you work toward it! diff --git a/README.md b/README.md index be3ae6a7b..342369a38 100644 --- a/README.md +++ b/README.md @@ -72,27 +72,27 @@ visualizer.score(X,y) visualizer.show() ``` -For additional information on getting started with Yellowbrick, view the quickstart guide in the [documentation](https://www.scikit-yb.org/en/latest/) and check out our [examples notebook](https://github.com/DistrictDataLabs/yellowbrick/blob/develop/examples/examples.ipynb). +For additional information on getting started with Yellowbrick, view the [Quick Start Guide](https://www.scikit-yb.org/en/latest/quickstart.html) in the [documentation](https://www.scikit-yb.org/en/latest/) and check out our [examples notebook](https://github.com/DistrictDataLabs/yellowbrick/blob/develop/examples/examples.ipynb). ## Contributing to Yellowbrick Yellowbrick is an open source project that is supported by a community who will gratefully and humbly accept any contributions you might make to the project. Large or small, any contribution makes a big difference; and if you've never contributed to an open source project before, we hope you will start with Yellowbrick! -If you are interested in contributing, check out our [contributor's guide](https://www.scikit-yb.org/en/latest/contributing.html). Beyond creating visualizers, there are many ways to contribute: +If you are interested in contributing, check out our [contributor's guide](https://www.scikit-yb.org/en/latest/contributing/index.html). Beyond creating visualizers, there are many ways to contribute: - Submit a bug report or feature request on [GitHub Issues](https://github.com/DistrictDataLabs/yellowbrick/issues). -- Contribute a Jupyter notebook to our examples[ gallery](https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples). +- Contribute a Jupyter notebook to our examples [gallery](https://github.com/DistrictDataLabs/yellowbrick/tree/develop/examples). - Assist us with [user testing](https://www.scikit-yb.org/en/latest/evaluation.html). - Add to the documentation or help with our website, [scikit-yb.org](https://www.scikit-yb.org). -- [Write unit or integration tests](https://www.scikit-yb.org/en/latest/contributing.html#testing) for our project. +- Write [unit or integration tests](https://www.scikit-yb.org/en/latest/contributing/developing_visualizers.html#integration-tests) for our project. - Answer questions on our issues, mailing list, Stack Overflow, and elsewhere. - Translate our documentation into another language. - Write a blog post, tweet, or share our project with others. -- Teach someone how to use Yellowbrick. +- [Teach](https://www.scikit-yb.org/en/latest/teaching.html) someone how to use Yellowbrick. As you can see, there are lots of ways to get involved and we would be very happy for you to join us! The only thing we ask is that you abide by the principles of openness, respect, and consideration of others as described in the [Python Software Foundation Code of Conduct](https://www.python.org/psf/codeofconduct/). -For more information, checkout the `CONTRIBUTING.md` file in the root of the repository or the detailed documentation at [Contributing to Yellowbrick](https://www.scikit-yb.org/en/latest/contributing.html) +For more information, checkout the `CONTRIBUTING.md` file in the root of the repository or the detailed documentation at [Contributing to Yellowbrick](https://www.scikit-yb.org/en/latest/contributing/index.html) ## Yellowbrick Datasets From ef52ad273f5ab6da1ebd29e353ae15e9a0e3b2f8 Mon Sep 17 00:00:00 2001 From: mmorrison1670 <46731745+mmorrison1670@users.noreply.github.com> Date: Sat, 30 Nov 2019 11:36:55 -0500 Subject: [PATCH 07/44] Changed pcoords to use show, added image comparison test, updated documentation (#991) Updates the parallel_coordinates quick method as part of Issue #960 --- docs/api/features/pcoords.rst | 33 ++++++++++++++++-- .../test_parallel_coordinates_quickmethod.png | Bin 0 -> 112249 bytes tests/test_features/test_pcoords.py | 10 ++++++ yellowbrick/features/pcoords.py | 22 ++++++++++++ 4 files changed, 63 insertions(+), 2 deletions(-) create mode 100644 tests/baseline_images/test_features/test_pcoords/test_parallel_coordinates_quickmethod.png diff --git a/docs/api/features/pcoords.rst b/docs/api/features/pcoords.rst index e3a302a46..5c7464d00 100644 --- a/docs/api/features/pcoords.rst +++ b/docs/api/features/pcoords.rst @@ -7,7 +7,12 @@ Parallel coordinates is multi-dimensional feature visualization technique where Data scientists use this method to detect clusters of instances that have similar classes, and to note features that have high variance or different distributions. We can see this in action after first loading our occupancy classification dataset. -.. note:: These visualizations can be produced with either the ``ParallelCoordinates`` visualizer or by using the ``parallel_coordinates`` quick method. +================= ================= +Visualizer `Parallel Coordinates `_ +Quick Method `parallel_coordinates `_ +Models Classification +Workflow Feature analysis +================= ================= .. plot:: :context: close-figs @@ -109,11 +114,35 @@ As you can see the "fast" drawing algorithm does not have the same build up of c .. image:: images/fast_parallel_coordinates_speedup.png +Quick Method +---------------------- +The same functionality above can be achieved with the associated quick method ``parallel_coordinates``. This method +will build the ``ParallelCoordinates`` object with the associated arguments, fit it, then (optionally) immediately +show it. + +.. plot:: + :context: close-figs + :alt: parallel_coordinates on the occupancy dataset + + from yellowbrick.features.pcoords import parallel_coordinates + from yellowbrick.datasets import load_occupancy + + # Load the classification data set + X, y = load_occupancy() + + # Specify the features of interest and the classes of the target + features = [ + "temperature", "relative humidity", "light", "CO2", "humidity" + ] + classes = ["unoccupied", "occupied"] + + # Instantiate the visualizer + visualizer = parallel_coordinates(X, y, classes=classes, features=features) API Reference ------------- .. automodule:: yellowbrick.features.pcoords - :members: ParallelCoordinates + :members: ParallelCoordinates, parallel_coordinates :undoc-members: :show-inheritance: diff --git 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visualizer = parallel_coordinates(X, y, show=False) + self.assert_images_similar(visualizer) + @pytest.mark.skipif(pd is None, reason="test requires pandas") def test_pandas_integration_sampled(self): """ diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index 18d620b9a..35f02ddfc 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -43,12 +43,15 @@ def parallel_coordinates( classes=None, normalize=None, sample=1.0, + random_state=None, + shuffle=False, colors=None, colormap=None, alpha=None, fast=False, vlines=True, vlines_kwds=None, + show=True, **kwargs ): """Displays each feature as a vertical axis and each instance as a line. @@ -89,6 +92,15 @@ def parallel_coordinates( If int, specifies the maximum number of samples to display. If float, specifies a fraction between 0 and 1 to display. + random_state : int, RandomState instance or None + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by np.random; only used if shuffle is True and sample < 1.0 + + shuffle : boolean, default: True + specifies whether sample is drawn randomly + colors : list or tuple, default: None optional list or tuple of colors to colorize lines Use either color to colorize the lines on a per class basis or @@ -116,6 +128,11 @@ def parallel_coordinates( vlines_kwds : dict, default: None options to style or display the vertical lines, default: None + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -145,6 +162,11 @@ def parallel_coordinates( visualizer.fit(X, y, **kwargs) visualizer.transform(X) + if show: + visualizer.show() + else: + visualizer.finalize() + # Return the visualizer object return visualizer From 85261ec01fbd536673d017501671385d760a55ea Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Wed, 11 Dec 2019 21:12:16 -0500 Subject: [PATCH 08/44] Fix breaking tests and update baseline images (#1000) * fix all the breaking tests, update baseline images as necessary * bump the tol for a few image comparison tests * accelerate pcoord and rfecv tests by running them on just the first hundred rows of data or so * specify liblinear solver for LR in feature importances * remove errant print statement * updated test_pcoords to use sample paramter and updated pcoords quick method to include shuffle and random_state parameters --- .../test_multiclass_class_report.png | Bin 25711 -> 23862 bytes .../test_confusion_matrix/test_fontsize.png | Bin 12298 -> 10566 bytes .../test_percent_mode.png | Bin 35181 -> 29807 bytes .../test_prcurve/test_binary_probability.png | Bin 7488 -> 8127 bytes .../test_custom_iso_f1_scores.png | Bin 28020 -> 27459 bytes .../test_quick_method_with_test_set.png | Bin 7412 -> 6704 bytes .../test_rocauc/test_binary_probability.png | Bin 36889 -> 29348 bytes .../test_rocauc/test_rocauc_quickmethod.png | Bin 19954 -> 19860 bytes ...nary_discrimination_threshold_alt_args.png | Bin 35796 -> 25929 bytes .../test_threshold/test_numpy_integration.png | Bin 39387 -> 38912 bytes .../test_pandas_integration.png | Bin 39387 -> 38912 bytes .../test_calinski_harabasz_metric.png | Bin 15146 -> 16225 bytes .../test_elbow/test_distortion_metric.png | Bin 17997 -> 17918 bytes .../test_elbow/test_locate_elbow.png | Bin 24179 -> 24206 bytes .../test_elbow/test_silhouette_metric.png | Bin 15715 -> 15792 bytes .../test_cluster/test_elbow/test_timings.png | Bin 38687 -> 38713 bytes .../test_affinity_tsne_no_legend.png | Bin 9623 -> 9648 bytes .../test_icdm/test_kmeans_mds.png | Bin 45235 -> 44341 bytes .../test_integrated_kmeans_silhouette.png | Bin 14615 -> 14536 bytes .../test_manifold_classification.png | Bin 51503 -> 51345 bytes .../test_manifold_classification_3d.png | Bin 75445 -> 73961 bytes .../test_manifold_regression.png | Bin 96653 -> 96189 bytes .../test_features/test_pca/test_biplot_2d.png | Bin 57660 -> 57091 bytes .../test_features/test_pca/test_biplot_3d.png | Bin 99080 -> 98783 bytes .../test_features/test_pca/test_discrete.png | Bin 84416 -> 84260 bytes .../test_features/test_pca/test_heatmap.png | Bin 80300 -> 80294 bytes .../test_pca_decomposition_quick_method.png | Bin 89520 -> 89175 bytes .../test_pca/test_scale_false_3d.png | Bin 91151 -> 91809 bytes .../test_pca/test_scale_true_2d.png | Bin 85198 -> 84976 bytes .../test_pca/test_scale_true_3d.png | Bin 95907 -> 95609 bytes .../test_parallel_coordinates_quickmethod.png | Bin 112249 -> 50075 bytes .../test_continuous_when_target_discrete.png | Bin 65661 -> 65619 bytes .../test_projection/test_discrete_3d.png | Bin 95748 -> 95346 bytes .../test_projection/test_discrete_plot.png | Bin 82251 -> 82253 bytes .../test_projection/test_single_plot.png | Bin 44706 -> 45000 bytes .../test_quick_method.png | Bin 6047 -> 6130 bytes .../test_cross_validation/test_regression.png | Bin 5572 -> 5709 bytes .../test_integration_quick_method.png | Bin 3595 -> 3593 bytes .../test_multi_coefs_stacked.png | Bin 3686 -> 3686 bytes .../test_learning_curve/test_classifier.png | Bin 20236 -> 16234 bytes .../test_learning_curve/test_clusters.png | Bin 28615 -> 27711 bytes .../test_learning_curve/test_regressor.png | Bin 17754 -> 19647 bytes .../test_rfecv/test_numpy_integration.png | Bin 20857 -> 13775 bytes .../test_rfecv/test_pandas_integration.png | Bin 20857 -> 13775 bytes .../test_rfecv/test_quick_method.png | Bin 23099 -> 21777 bytes .../test_rfecv/test_rfecv_classification.png | Bin 26044 -> 22024 bytes .../test_rfecv/test_rfecv_step.png | Bin 24653 -> 22163 bytes .../test_alphas/test_similar_image.png | Bin 18731 -> 18479 bytes .../test_integrated_yb_colormap.png | Bin 14299 -> 14251 bytes .../test_numpy_occupancy_compare.png | Bin 4165 -> 4178 bytes .../test_pandas_occupancy_compare.png | Bin 4165 -> 4178 bytes .../test_integrated_freqdist.png | Bin 10168 -> 10151 bytes .../test_make_classification_tsne.png | Bin 27762 -> 28302 bytes ..._make_classification_tsne_class_labels.png | Bin 27762 -> 28302 bytes .../test_tsne/test_no_target_tsne.png | Bin 23063 -> 21171 bytes .../test_tsne/test_visualizer_with_pandas.png | Bin 28841 -> 29337 bytes .../test_classification_report.py | 26 +++++++------- .../test_classifier/test_confusion_matrix.py | 24 ++++++------- tests/test_classifier/test_rocauc.py | 2 +- tests/test_classifier/test_threshold.py | 19 +++++++--- tests/test_cluster/test_elbow.py | 33 +++++++++++++++--- tests/test_features/test_pcoords.py | 5 +-- .../test_cross_validation.py | 14 ++------ .../test_model_selection/test_importances.py | 4 ++- .../test_learning_curve.py | 2 +- tests/test_model_selection/test_rfecv.py | 31 +++++++++------- tests/test_target/test_class_balance.py | 4 +-- tests/test_text/test_freqdist.py | 2 +- yellowbrick/features/pcoords.py | 2 ++ yellowbrick/features/projection.py | 2 ++ 70 files changed, 105 insertions(+), 65 deletions(-) diff --git a/tests/baseline_images/test_classifier/test_classification_report/test_multiclass_class_report.png b/tests/baseline_images/test_classifier/test_classification_report/test_multiclass_class_report.png index 42829ff4cd7d80ad825a3ceff8432f17625a364c..22fbda1a6b8df7594e04045c4871c46978ea82ce 100644 GIT binary patch literal 23862 zcmeFZcT|&Kx9^J;8(_miRcQhODowhIfT8zJA}YO?(3>bAAfWUP(jkOM4Fp0Ie)QfF 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z40pdm`Y!nCj6@&-_}oo&GDk@dv>x9)Ru+j*1|<|D@1=#^L|?EdL|=uW;qR%}h(@c+V*6 VyZX8=5o6meGwgxuqo{|gNNWxN0Y diff --git a/tests/test_classifier/test_classification_report.py b/tests/test_classifier/test_classification_report.py index 8f6e3570a..cf279a13f 100644 --- a/tests/test_classifier/test_classification_report.py +++ b/tests/test_classifier/test_classification_report.py @@ -87,28 +87,28 @@ def test_multiclass_class_report(self): assert viz.scores_ == { "precision": { - 0: 0.5333333333333333, - 1: 0.5, + 0: 0.75, + 1: 0.47368421052631576, 2: 0.45, - 3: 0.4, - 4: 0.4, - 5: 0.5882352941176471, + 3: 0.375, + 4: 0.5, + 5: 0.5294117647058824, }, "recall": { - 0: 0.42105263157894735, + 0: 0.47368421052631576, 1: 0.5625, 2: 0.6428571428571429, 3: 0.3157894736842105, - 4: 0.375, - 5: 0.625, + 4: 0.5, + 5: 0.5625, }, "f1": { - 0: 0.47058823529411764, - 1: 0.5294117647058824, + 0: 0.5806451612903226, + 1: 0.5142857142857142, 2: 0.5294117647058824, - 3: 0.35294117647058826, - 4: 0.38709677419354843, - 5: 0.6060606060606061, + 3: 0.34285714285714286, + 4: 0.5, + 5: 0.5454545454545455, }, } diff --git a/tests/test_classifier/test_confusion_matrix.py b/tests/test_classifier/test_confusion_matrix.py index 4e96f434f..dc40d3f38 100644 --- a/tests/test_classifier/test_confusion_matrix.py +++ b/tests/test_classifier/test_confusion_matrix.py @@ -99,13 +99,13 @@ def test_confusion_matrix(self): [38, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 35, 0, 0, 0, 0, 0, 0, 2, 0], [0, 0, 39, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 38, 0, 1, 0, 0, 2, 0], + [0, 0, 0, 38, 0, 0, 0, 0, 3, 0], [0, 0, 0, 0, 40, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 27, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 29, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 35, 0, 1], - [0, 2, 0, 0, 0, 0, 0, 0, 32, 0], - [0, 0, 0, 0, 0, 0, 0, 1, 1, 35], + [0, 0, 0, 0, 0, 1, 0, 1, 31, 1], + [0, 0, 0, 0, 0, 0, 0, 0, 1, 36], ] ), ) @@ -176,16 +176,16 @@ def test_percent_mode(self): cm.confusion_matrix_, np.array( [ - [16, 0, 0, 0, 0, 22, 0, 0, 0, 0], - [0, 11, 0, 0, 0, 26, 0, 0, 0, 0], - [0, 0, 10, 0, 0, 29, 0, 0, 0, 0], - [0, 0, 0, 6, 0, 35, 0, 0, 0, 0], - [0, 0, 0, 0, 11, 30, 0, 0, 0, 0], + [38, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 37, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 39, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 38, 0, 1, 0, 1, 1, 0], + [0, 0, 0, 0, 41, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 27, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 9, 21, 0, 0, 0], - [0, 0, 0, 0, 0, 29, 0, 7, 0, 0], - [0, 0, 0, 0, 0, 32, 0, 0, 2, 0], - [0, 0, 0, 0, 0, 34, 0, 0, 0, 3], + [0, 0, 0, 0, 0, 0, 30, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 35, 0, 1], + [0, 0, 0, 0, 0, 0, 0, 0, 34, 0], + [0, 0, 0, 0, 0, 0, 0, 1, 0, 36], ] ), ) diff --git a/tests/test_classifier/test_rocauc.py b/tests/test_classifier/test_rocauc.py index 2655fde80..3a862fb6d 100644 --- a/tests/test_classifier/test_rocauc.py +++ b/tests/test_classifier/test_rocauc.py @@ -214,7 +214,7 @@ def test_rocauc_quickmethod(self): X, y = load_occupancy(return_dataset=True).to_numpy() model = LogisticRegression() - #compare the images + # compare the images visualizer = roc_auc(model, X, y, show=False) self.assert_images_similar(visualizer) diff --git a/tests/test_classifier/test_threshold.py b/tests/test_classifier/test_threshold.py index 810d3ea07..873a54495 100644 --- a/tests/test_classifier/test_threshold.py +++ b/tests/test_classifier/test_threshold.py @@ -31,10 +31,10 @@ from tests.base import VisualTestCase from numpy.testing.utils import assert_array_equal +from sklearn.base import ClassifierMixin from sklearn.svm import LinearSVC, NuSVC from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier -from sklearn.neighbors import RadiusNeighborsClassifier from sklearn.naive_bayes import BernoulliNB, GaussianNB from sklearn.linear_model import Ridge, LogisticRegression from sklearn.model_selection import StratifiedShuffleSplit @@ -44,7 +44,6 @@ except ImportError: pd = None - ########################################################################## ## DiscriminationThreshold Test Cases ########################################################################## @@ -257,11 +256,21 @@ def test_requires_probabilistic_classifier(self): Assert requires probabilistic classifier """ message = "requires a probabilistic binary classifier" - assert is_classifier(RadiusNeighborsClassifier) - assert not is_probabilistic(RadiusNeighborsClassifier) + + class RoboClassifier(ClassifierMixin): + """ + Dummy Non-Probabilistic Classifier + """ + + def fit(self, X, y): + self.classes_ = [0, 1] + return self + + assert is_classifier(RoboClassifier) + assert not is_probabilistic(RoboClassifier) with pytest.raises(yb.exceptions.YellowbrickError, match=message): - DiscriminationThreshold(RadiusNeighborsClassifier()) + DiscriminationThreshold(RoboClassifier()) def test_accepts_predict_proba(self): """ diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py index 81f3579ca..c3431e531 100644 --- a/tests/test_cluster/test_elbow.py +++ b/tests/test_cluster/test_elbow.py @@ -235,7 +235,14 @@ def test_distortion_metric(self): ) visualizer.fit(self.clusters.X) - expected = np.array([69.100065, 54.081571, 43.146921, 34.978487]) + expected = np.array( + [ + 69.10006514142941, + 54.081571290449936, + 44.491830981793605, + 33.99887993254433, + ] + ) assert len(visualizer.k_scores_) == 4 visualizer.finalize() @@ -256,7 +263,14 @@ def test_silhouette_metric(self): ) visualizer.fit(self.clusters.X) - expected = np.array([0.691636, 0.456646, 0.255174, 0.239842]) + expected = np.array( + [ + 0.6916363804000003, + 0.456645663683503, + 0.26918583373704463, + 0.25523298106687914, + ] + ) assert len(visualizer.k_scores_) == 4 visualizer.finalize() @@ -280,7 +294,12 @@ def test_calinski_harabasz_metric(self): assert visualizer.elbow_value_ is None expected = np.array( - [81.662726256035683, 50.992378259195554, 40.952179227847012, 35.939494] + [ + 81.66272625603568, + 50.992378259195554, + 39.573201061900455, + 37.06865804955547, + ] ) visualizer.finalize() @@ -306,7 +325,13 @@ def test_locate_elbow(self): assert len(visualizer.k_scores_) == 5 assert visualizer.elbow_value_ == 3 expected = np.array( - [4286.479848, 12463.383743, 8766.999551, 6950.08391, 5865.79722] + [ + 4286.4798481306625, + 12463.383743070379, + 8763.75791732466, + 6942.167328461612, + 5859.608884917707, + ] ) visualizer.finalize() diff --git a/tests/test_features/test_pcoords.py b/tests/test_features/test_pcoords.py index 94d3e4850..dfa93fdfe 100644 --- a/tests/test_features/test_pcoords.py +++ b/tests/test_features/test_pcoords.py @@ -173,8 +173,9 @@ def test_parallel_coordinates_quickmethod(self): """ X, y = load_occupancy(return_dataset=True).to_numpy() - #compare the images - visualizer = parallel_coordinates(X, y, show=False) + # Compare the images + # Use only the first 100 samples so the test will run faster + visualizer = parallel_coordinates(X, y, sample=100, show=False) self.assert_images_similar(visualizer) @pytest.mark.skipif(pd is None, reason="test requires pandas") diff --git a/tests/test_model_selection/test_cross_validation.py b/tests/test_model_selection/test_cross_validation.py index a3582c5f5..0eae0d466 100644 --- a/tests/test_model_selection/test_cross_validation.py +++ b/tests/test_model_selection/test_cross_validation.py @@ -93,12 +93,9 @@ def test_classifier_with_cv(self): """ X, y = self.classification - cv = ShuffleSplit(3, random_state=288) - + cv = StratifiedKFold(5, random_state=288) oz_external_cv = CVScores(LogisticRegressionCV(), cv=cv) - oz_internal_cv = CVScores(LogisticRegressionCV(cv=cv)) - oz_external_cv.fit(X, y) oz_internal_cv.fit(X, y) @@ -112,10 +109,8 @@ def test_regression(self): """ X, y = self.regression - cv = ShuffleSplit(3, random_state=938) - + cv = ShuffleSplit(5, random_state=938) oz = CVScores(DecisionTreeRegressor(random_state=23), cv=cv, scoring="r2") - oz.fit(X, y) oz.finalize() @@ -127,12 +122,9 @@ def test_regressor_with_cv(self): """ X, y = self.regression - cv = ShuffleSplit(3, random_state=288) - + cv = ShuffleSplit(5, random_state=288) oz_external_cv = CVScores(RidgeCV(), cv=cv) - oz_internal_cv = CVScores(RidgeCV(cv=cv)) - oz_external_cv.fit(X, y) oz_internal_cv.fit(X, y) diff --git a/tests/test_model_selection/test_importances.py b/tests/test_model_selection/test_importances.py index 4463e0152..c829e926e 100644 --- a/tests/test_model_selection/test_importances.py +++ b/tests/test_model_selection/test_importances.py @@ -230,7 +230,9 @@ def test_multi_coefs_stacked(self): """ X, y = load_iris(True) - viz = FeatureImportances(LogisticRegression(random_state=222), stack=True) + viz = FeatureImportances( + LogisticRegression(solver="liblinear", random_state=222), stack=True + ) viz.fit(X, y) viz.finalize() diff --git a/tests/test_model_selection/test_learning_curve.py b/tests/test_model_selection/test_learning_curve.py index 422db91dd..7f9e5fb36 100644 --- a/tests/test_model_selection/test_learning_curve.py +++ b/tests/test_model_selection/test_learning_curve.py @@ -92,7 +92,7 @@ def test_classifier(self): ).fit(X, y) oz.finalize() - self.assert_images_similar(oz) + self.assert_images_similar(oz, tol=0.1) # w/o tol fails with RMS 0.037 @pytest.mark.xfail(sys.platform == "win32", reason="images not close on windows") def test_regressor(self): diff --git a/tests/test_model_selection/test_rfecv.py b/tests/test_model_selection/test_rfecv.py index 8405011c6..3b27b975a 100644 --- a/tests/test_model_selection/test_rfecv.py +++ b/tests/test_model_selection/test_rfecv.py @@ -54,12 +54,11 @@ def dataset(request): Creates a multiclass classification dataset fixture for RFECV """ X, y = make_classification( - n_samples=600, - n_features=15, - n_informative=7, - n_redundant=4, + n_samples=300, + n_features=5, + n_informative=3, n_repeated=0, - n_classes=8, + n_classes=4, n_clusters_per_class=1, random_state=0, ) @@ -128,7 +127,7 @@ def test_rfecv_classification(self): @pytest.mark.filterwarnings("ignore:F-score is ill-defined") def test_quick_method(self): """ - Test the recv quick method works with LogisticRegression + Test the rfecv quick method works with LogisticRegression """ cv = ShuffleSplit(2, random_state=14) model = LogisticRegression() @@ -147,12 +146,16 @@ def test_pandas_integration(self): data = load_occupancy(return_dataset=True) X, y = data.to_pandas() - assert isinstance(X, pd.DataFrame) - assert isinstance(y, pd.Series) + # Use only the first 100 samples so the test will run faster + X_t = X[:100] + y_t = y[:100] + + assert isinstance(X_t, pd.DataFrame) + assert isinstance(y_t, pd.Series) cv = StratifiedKFold(n_splits=4, random_state=32) oz = RFECV(RandomForestClassifier(random_state=83), cv=cv) - oz.fit(X, y) + oz.fit(X_t, y_t) oz.finalize() self.assert_images_similar(oz, remove_legend=True) @@ -165,12 +168,16 @@ def test_numpy_integration(self): data = load_occupancy(return_dataset=True) X, y = data.to_numpy() - assert isinstance(X, np.ndarray) - assert isinstance(y, np.ndarray) + # Use only the first 100 samples so the test will run faster + X_t = X[:100] + y_t = y[:100] + + assert isinstance(X_t, np.ndarray) + assert isinstance(y_t, np.ndarray) cv = StratifiedKFold(n_splits=4, random_state=32) oz = RFECV(RandomForestClassifier(random_state=83), cv=cv) - oz.fit(X, y) + oz.fit(X_t, y_t) oz.finalize() self.assert_images_similar(oz, remove_legend=True) diff --git a/tests/test_target/test_class_balance.py b/tests/test_target/test_class_balance.py index 216cf3fe1..aa4802115 100644 --- a/tests/test_target/test_class_balance.py +++ b/tests/test_target/test_class_balance.py @@ -211,7 +211,7 @@ def test_pandas_occupancy_compare(self): assert oz.fit(y_train, y_test) is oz # oz.finalize() - self.assert_images_similar(oz) + self.assert_images_similar(oz, tol=0.5) # w/o tol fails with RMS 0.433 def test_numpy_occupancy_compare(self): """ @@ -227,7 +227,7 @@ def test_numpy_occupancy_compare(self): assert oz.fit(y_train, y_test) is oz # oz.finalize() - self.assert_images_similar(oz) + self.assert_images_similar(oz, tol=0.5) # w/o tol fails with RMS 0.433 def test_quick_method(self): """ diff --git a/tests/test_text/test_freqdist.py b/tests/test_text/test_freqdist.py index 2679703fe..d5129ae9b 100644 --- a/tests/test_text/test_freqdist.py +++ b/tests/test_text/test_freqdist.py @@ -54,4 +54,4 @@ def test_integrated_freqdist(self): visualizer.fit(docs) visualizer.finalize() - self.assert_images_similar(visualizer) + self.assert_images_similar(visualizer, tol=0.5) # w/o tol fails with RMS 0.121 diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index 35f02ddfc..5cf433211 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -149,6 +149,8 @@ def parallel_coordinates( classes=classes, normalize=normalize, sample=sample, + random_state=random_state, + shuffle=shuffle, colors=colors, colormap=colormap, alpha=alpha, diff --git a/yellowbrick/features/projection.py b/yellowbrick/features/projection.py index 6f3120679..7173d9995 100644 --- a/yellowbrick/features/projection.py +++ b/yellowbrick/features/projection.py @@ -286,6 +286,8 @@ def finalize(self): if self.colorbar: if self.projection == 3: sm = plt.cm.ScalarMappable(cmap=self._colors, norm=self._norm) + # Avoid MPL TypeError: "You must first set_array for mappable" + sm.set_array([]) self.cbar = plt.colorbar(sm, ax=self.ax) else: From c725a7c2e96fa19f70724938d239e35e09948b32 Mon Sep 17 00:00:00 2001 From: mchestnut91 <40185521+mchestnut91@users.noreply.github.com> Date: Wed, 11 Dec 2019 22:48:53 -0500 Subject: [PATCH 09/44] Tsne quick method (#997) * added to tsne quick method and docstring * added image comparison test and documentation fot tsne quick method * making requested changes --- docs/api/text/tsne.rst | 30 +++++++++++++++-- .../test_tsne/test_integrated_tsne.png | Bin 45898 -> 44594 bytes .../test_text/test_tsne/test_quick_method.png | Bin 0 -> 57672 bytes tests/test_text/test_tsne.py | 15 +++++++++ yellowbrick/text/tsne.py | 31 ++++++++++++++---- 5 files changed, 68 insertions(+), 8 deletions(-) create mode 100644 tests/baseline_images/test_text/test_tsne/test_quick_method.png diff --git a/docs/api/text/tsne.rst b/docs/api/text/tsne.rst index 78bb038a9..16f38c7e3 100644 --- a/docs/api/text/tsne.rst +++ b/docs/api/text/tsne.rst @@ -3,13 +3,19 @@ t-SNE Corpus Visualization ========================== +================= ================= +Visualizer `TSNEVisualizer `_ +Quick Method `tsne() `_ +Models Decomposition +Workflow Feature Engineering/Selection +================= ================= + One very popular method for visualizing document similarity is to use t-distributed stochastic neighbor embedding, t-SNE. Scikit-learn implements this decomposition method as the ``sklearn.manifold.TSNE`` transformer. By decomposing high-dimensional document vectors into 2 dimensions using probability distributions from both the original dimensionality and the decomposed dimensionality, t-SNE is able to effectively cluster similar documents. By decomposing to 2 or 3 dimensions, the documents can be visualized with a scatter plot. Unfortunately, ``TSNE`` is very expensive, so typically a simpler decomposition method such as SVD or PCA is applied ahead of time. The ``TSNEVisualizer`` creates an inner transformer pipeline that applies such a decomposition first (SVD with 50 components by default), then performs the t-SNE embedding. The visualizer then plots the scatter plot, coloring by cluster or by class, or neither if a structural analysis is required. After importing the required tools, we can use the :doc:`hobbies corpus <../datasets/hobbies>` and vectorize the text using TF-IDF. Once the corpus is vectorized we can visualize it, showing the distribution of classes. - .. plot:: :context: close-figs :alt: TSNE Plot @@ -87,11 +93,31 @@ This means we don't have to use class labels at all. Instead we can use cluster tsne.fit(X, ["c{}".format(c) for c in clusters.labels_]) tsne.show() +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method ``tsne``. This method will build the ``TSNEVisualizer`` object with the associated arguments, fit it, then (optionally) immediately show it + +.. plot:: + :context: close-figs + :alt: tsne quick method on the hobbies dataset + + from yellowbrick.text.tsne import tsne + from sklearn.feature_extraction.text import TfidfVectorizer + from yellowbrick.datasets import load_hobbies + + # Load the data and create document vectors + corpus = load_hobbies() + tfidf = TfidfVectorizer() + + X = tfidf.fit_transform(corpus.data) + y = corpus.target + + tsne(X, y) API Reference ------------- .. automodule:: yellowbrick.text.tsne - :members: TSNEVisualizer + :members: TSNEVisualizer, tsne :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_text/test_tsne/test_integrated_tsne.png b/tests/baseline_images/test_text/test_tsne/test_integrated_tsne.png index 39d843eb5581a26c9ea13456edd9006de6163162..1b4ccab136548a9076dd507c78bac75e6cdb64d5 100644 GIT binary patch literal 44594 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scatter_kwargs = tsne.ax.scatter.call_args assert "alpha" in scatter_kwargs assert scatter_kwargs["alpha"] == 0.5 + + def test_quick_method(self): + """ + Test for tsne quick method with hobbies dataset + """ + corpus = load_hobbies() + tfidf = TfidfVectorizer() + + X = tfidf.fit_transform(corpus.data) + y = corpus.target + + viz = tsne(X, y, show=False) + + self.assert_images_similar(viz, tol=50) + diff --git a/yellowbrick/text/tsne.py b/yellowbrick/text/tsne.py index 50ccaf435..ead496eb4 100644 --- a/yellowbrick/text/tsne.py +++ b/yellowbrick/text/tsne.py @@ -42,10 +42,11 @@ def tsne( ax=None, decompose="svd", decompose_by=50, - classes=None, + labels=None, colors=None, colormap=None, alpha=0.7, + show=True, **kwargs ): """ @@ -81,7 +82,7 @@ def tsne( Specify the number of components for preliminary decomposition, by default this is 50; the more components, the slower TSNE will be. - classes : list of strings + labels : list of strings The names of the classes in the target, used to create a legend. colors : list or tuple of colors @@ -94,9 +95,25 @@ def tsne( Specify a transparency where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Pass any additional keyword arguments to the TSNE transformer. + Example + -------- + >>> from yellowbrick.text.tsne import tsne + >>> from sklearn.feature_extraction.text import TfidfVectorizer + >>> from yellowbrick.datasets import load_hobbies + >>> corpus = load_hobbies() + >>> tfidf = TfidfVectorizer() + >>> X = tfidf.fit_transform(corpus.data) + >>> y = corpus.target + >>> tsne(X, y) + Returns ------- visualizer: TSNEVisualizer @@ -107,7 +124,7 @@ def tsne( ax=ax, decompose=decompose, decompose_by=decompose_by, - classes=classes, + labels=labels, colors=colors, colormap=colormap, alpha=alpha, @@ -116,13 +133,15 @@ def tsne( # Fit and transform the visualizer (calls draw) visualizer.fit(X, y, **kwargs) - visualizer.transform(X) - visualizer.finalize() + + if show: + visualizer.show() + else: + visualizer.finalize() # Return the visualizer object return visualizer - ########################################################################## ## TSNEVisualizer ########################################################################## From c2028de2b7703e563503f7e85fdd65ad08de1ef6 Mon Sep 17 00:00:00 2001 From: Michael Garod Date: Mon, 16 Dec 2019 17:29:53 -0500 Subject: [PATCH 10/44] Dispersion quick method API Update (#989) * Add show to quick method Also update the arg and docstring ordering for both the visualizer and quick method so that they are in sync * Add documentation for dispersion quick method Change argument name from words to target_words to maintain consistency Add at-a-glance box in dispersion documentation * Fix typos * Change test to use new param * Change dispersion workflow to feature engineering * Add quick method header * small doc change * Do not show quick method plot. Make import explicit. --- docs/api/text/dispersion.rst | 31 ++++++++++++++++++- docs/api/text/index.rst | 2 +- tests/test_text/test_dispersion.py | 6 ++-- yellowbrick/text/dispersion.py | 49 +++++++++++++++++------------- 4 files changed, 62 insertions(+), 26 deletions(-) diff --git a/docs/api/text/dispersion.rst b/docs/api/text/dispersion.rst index 445dc61c8..031dbd29b 100644 --- a/docs/api/text/dispersion.rst +++ b/docs/api/text/dispersion.rst @@ -5,6 +5,13 @@ Dispersion Plot A word's importance can be weighed by its dispersion in a corpus. Lexical dispersion is a measure of a word's homogeneity across the parts of a corpus. This plot notes the occurrences of a word and how many words from the beginning of the corpus it appears. +================= ============================== +Visualizer `DispersionPlot `_ +Quick Method `dispersion() `_ +Models Text Modeling +Workflow Feature Engineering +================= ============================== + .. plot:: :context: close-figs :alt: Dispersion Plot @@ -26,11 +33,33 @@ A word's importance can be weighed by its dispersion in a corpus. Lexical dispe visualizer.fit(text) visualizer.show() +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method `dispersion`. This method will build the Dispersion Plot object with the associated arguments, fit it, then (optionally) immediately show the visualization. + +.. plot:: + :context: close-figs + :alt: Quick Method Dispersion Plot + + from yellowbrick.text import DispersionPlot, dispersion + from yellowbrick.datasets import load_hobbies + + # Load the text data + corpus = load_hobbies() + + # Create a list of words from the corpus text + text = [doc.split() for doc in corpus.data] + + # Choose words whose occurence in the text will be plotted + target_words = ['features', 'mobile', 'cooperative', 'competitive', 'combat', 'online'] + + # Create the visualizer and draw the plot + dispersion(target_words, text) API Reference ------------- .. automodule:: yellowbrick.text.dispersion - :members: DispersionPlot + :members: DispersionPlot, dispersion :undoc-members: :show-inheritance: diff --git a/docs/api/text/index.rst b/docs/api/text/index.rst index 5166c6aed..94defb6a2 100644 --- a/docs/api/text/index.rst +++ b/docs/api/text/index.rst @@ -5,7 +5,7 @@ Text Modeling Visualizers Yellowbrick provides the ``yellowbrick.text`` module for text-specific visualizers. The ``TextVisualizer`` class specifically deals with datasets that are corpora and not simple numeric arrays or DataFrames, providing utilities for analyzing word dispersion and distribution, showing document similarity, or simply wrapping some of the other standard visualizers with text-specific display properties. -We currently have four text-specific visualizations implemented: +We currently have five text-specific visualizations implemented: - :doc:`freqdist`: plot the frequency of tokens in a corpus - :doc:`tsne`: plot similar documents closer together to discover clusters diff --git a/tests/test_text/test_dispersion.py b/tests/test_text/test_dispersion.py index 09962b6e7..49321e1cd 100644 --- a/tests/test_text/test_dispersion.py +++ b/tests/test_text/test_dispersion.py @@ -22,7 +22,7 @@ from yellowbrick.exceptions import YellowbrickValueError from yellowbrick.datasets import load_hobbies -from yellowbrick.text.dispersion import * +from yellowbrick.text.dispersion import DispersionPlot, dispersion from tests.base import VisualTestCase @@ -40,14 +40,14 @@ class TestDispersionPlot(VisualTestCase): def test_quick_method(self): """ - Assert no errors occur when using the qucik method + Assert no errors occur when using the quick method """ _, ax = plt.subplots() text = [doc.split() for doc in corpus.data] target_words = ["Game", "player", "score", "oil", "Man"] - viz = dispersion(words=target_words, corpus=text, ax=ax) + viz = dispersion(target_words=target_words, corpus=text, ax=ax, show=False) viz.ax.grid(False) self.assert_images_similar(viz, tol=25) diff --git a/yellowbrick/text/dispersion.py b/yellowbrick/text/dispersion.py index d36cafe20..4f7be3854 100644 --- a/yellowbrick/text/dispersion.py +++ b/yellowbrick/text/dispersion.py @@ -48,10 +48,6 @@ class DispersionPlot(TextVisualizer): ax : matplotlib axes, default: None The axes to plot the figure on. - labels : list of strings - The names of the classes in the target, used to create a legend. - Labels must match names of classes in sorted order. - colors : list or tuple of colors Specify the colors for each individual class @@ -65,6 +61,10 @@ class DispersionPlot(TextVisualizer): Specify whether document boundaries will be displayed. Vertical lines are positioned at the end of each document. + labels : list of strings + The names of the classes in the target, used to create a legend. + Labels must match names of classes in sorted order. + kwargs : dict Pass any additional keyword arguments to the super class. @@ -80,10 +80,10 @@ def __init__( target_words, ax=None, colors=None, + colormap=None, ignore_case=False, annotate_docs=False, labels=None, - colormap=None, **kwargs ): super(DispersionPlot, self).__init__(ax=ax, **kwargs) @@ -275,17 +275,17 @@ def finalize(self, **kwargs): ## Quick Method ########################################################################## - def dispersion( - words, + target_words, corpus, y=None, ax=None, colors=None, colormap=None, - labels=None, annotate_docs=False, ignore_case=False, + labels=None, + show=True, **kwargs ): """ Displays lexical dispersion plot for words in a corpus @@ -296,25 +296,21 @@ def dispersion( Parameters ---------- - words : list + target_words : list A list of words whose dispersion will be examined within a corpus + corpus : list + Should be provided as a list of documents that contain + a list of words in the order they appear in the document. + y : ndarray or Series of length n An optional array or series of target or class values for instances. If this is specified, then the points will be colored according to their class. - corpus : list - Should be provided as a list of documents that contain - a list of words in the order they appear in the document. - ax : matplotlib axes, default: None The axes to plot the figure on. - labels : list of strings - The names of the classes in the target, used to create a legend. - Labels must match names of classes in sorted order. - colors : list or tuple of colors Specify the colors for each individual class @@ -328,6 +324,15 @@ def dispersion( ignore_case : boolean, default: False Specify whether input will be case-sensitive. + labels : list of strings + The names of the classes in the target, used to create a legend. + Labels must match names of classes in sorted order. + + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however + you cannot call ``plt.savefig`` from this signature, nor + ``clear_figure``. If False, simply calls ``finalize()`` + kwargs : dict Pass any additional keyword arguments to the super class. @@ -339,7 +344,7 @@ def dispersion( # Instantiate the visualizer visualizer = DispersionPlot( - words, + target_words, ax=ax, colors=colors, colormap=colormap, @@ -349,9 +354,11 @@ def dispersion( **kwargs ) - # Fit and transform the visualizer (calls draw) visualizer.fit(corpus, y, **kwargs) - visualizer.finalize() - # Return the visualizer object + if show: + visualizer.show() + else: + visualizer.finalize() + return visualizer From 9d88ab216e9d05967ecffebd017970c5f2fad242 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Wed, 18 Dec 2019 09:55:04 -0500 Subject: [PATCH 11/44] Attribution and copyright --- LICENSE.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LICENSE.txt b/LICENSE.txt index a76190a7f..4b9c1a73b 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -186,7 +186,7 @@ same "printed page" as the copyright notice for easier identification within third-party archives. - Copyright 2016 District Data Labs + Copyright 2016-2020 The scikit-yb developers Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. From 46544ca782d24b0d11607bd7aad7c77047418a55 Mon Sep 17 00:00:00 2001 From: mmorrison1670 <46731745+mmorrison1670@users.noreply.github.com> Date: Sat, 4 Jan 2020 14:07:44 -0500 Subject: [PATCH 12/44] Updated PCA quick method (#999) Updates the PCA quick method and adds additional information about its use to the documentation. --- docs/api/features/pca.rst | 43 +++++++++++++++++++++++++++------ tests/test_features/test_pca.py | 4 +-- yellowbrick/features/pca.py | 16 +++++++++--- 3 files changed, 51 insertions(+), 12 deletions(-) diff --git a/docs/api/features/pca.rst b/docs/api/features/pca.rst index d0b37011f..9d512fa1d 100644 --- a/docs/api/features/pca.rst +++ b/docs/api/features/pca.rst @@ -5,12 +5,19 @@ PCA Projection The PCA Decomposition visualizer utilizes principal component analysis to decompose high dimensional data into two or three dimensions so that each instance can be plotted in a scatter plot. The use of PCA means that the projected dataset can be analyzed along axes of principal variation and can be interpreted to determine if spherical distance metrics can be utilized. +================= ================= +Visualizer `PCA Decomposition `_ +Quick Method `pca_decomposition `_ +Models Classification/Regression +Workflow Feature Engineering/Selection +================= ================= + .. plot:: :context: close-figs :alt: PCA Projection, 2D from yellowbrick.datasets import load_credit - from yellowbrick.features.pca import PCADecomposition + from yellowbrick.features import PCADecomposition # Specify the features of interest and the target X, y = load_credit() @@ -30,13 +37,13 @@ The PCA projection can also be plotted in three dimensions to attempt to visuali :alt: PCA Projection, 3D from yellowbrick.datasets import load_credit - from yellowbrick.features.pca import PCADecomposition + from yellowbrick.features import PCADecomposition X, y = load_credit() colors = np.array(['r' if yi else 'b' for yi in y]) - visualizer = PCADecomposition(scale=True, color=colors, proj_dim=3) + visualizer = PCADecomposition(scale=True, color=colors, projection=3) visualizer.fit_transform(X, y) visualizer.show() @@ -51,7 +58,7 @@ The PCA projection can be enhanced to a biplot whose points are the projected in :alt: PCA biplot projection, 2D from yellowbrick.datasets import load_concrete - from yellowbrick.features.pca import PCADecomposition + from yellowbrick.features import PCADecomposition # Load the concrete dataset X, y = load_concrete() @@ -66,18 +73,40 @@ The PCA projection can be enhanced to a biplot whose points are the projected in :alt: PCA biplot projection, 3D from yellowbrick.datasets import load_concrete - from yellowbrick.features.pca import PCADecomposition + from yellowbrick.features import PCADecomposition X, y = load_concrete() - visualizer = PCADecomposition(scale=True, proj_features=True, proj_dim=3) + visualizer = PCADecomposition(scale=True, proj_features=True, projection=3) visualizer.fit_transform(X, y) visualizer.show() +Quick Method +---------------------- +The same functionality above can be achieved with the associated quick method ``pca_decomposition``. This method +will build the ``PCADecomposition`` object with the associated arguments, fit it, then (optionally) immediately +show it. + +.. plot:: + :context: close-figs + :alt: pca_decomposition on the credit dataset + + from yellowbrick.datasets import load_credit + from yellowbrick.features import pca_decomposition + + # Specify the features of interest and the target + X, y = load_credit() + + # Create a list of colors to assign to points in the plot + colors = np.array(['r' if yi else 'b' for yi in y]) + + # Instantiate the visualizer + visualizer = pca_decomposition(X, y, scale=True, color=colors) + API Reference ------------- .. automodule:: yellowbrick.features.pca - :members: PCA + :members: PCA, pca_decomposition :undoc-members: :show-inheritance: diff --git a/tests/test_features/test_pca.py b/tests/test_features/test_pca.py index ee8242230..eb1d28dde 100644 --- a/tests/test_features/test_pca.py +++ b/tests/test_features/test_pca.py @@ -122,10 +122,10 @@ def test_transform_without_fit(self): @pytest.mark.xfail(IS_WINDOWS_OR_CONDA, reason="RMS of 12.115 on miniconda") def test_pca_decomposition_quick_method(self): """ - Test the quick method PCA visualizer 2 dimensions scaled. + Test the quick method PCA visualizer """ visualizer = pca_decomposition( - *self.discrete, projection=2, scale=True, random_state=28 + *self.discrete, projection=2, scale=True, random_state=28, show=False ) # AppVeyor tests fail with RMS 12.115 diff --git a/yellowbrick/features/pca.py b/yellowbrick/features/pca.py index 44db15b98..6b09dfca5 100644 --- a/yellowbrick/features/pca.py +++ b/yellowbrick/features/pca.py @@ -292,7 +292,7 @@ def transform(self, X, y=None, **kwargs): Calls the internal `transform` method of the scikit-learn PCA transformer, which performs a dimensionality reduction on the input features ``X``. Next calls the ``draw`` method of the Yellowbrick visualizer, finally returning a new array of - transformed features of shape ``(len(X), proj_dim)``. + transformed features of shape ``(len(X), projection)``. Parameters ---------- @@ -306,7 +306,7 @@ def transform(self, X, y=None, **kwargs): ------- Xp : ndarray or DataFrame of shape n x m Returns a new array-like object of transformed features of shape - ``(len(X), proj_dim)``. + ``(len(X), projection)``. """ try: Xp = self.pca_transformer.transform(X) @@ -468,6 +468,7 @@ def pca_decomposition( random_state=None, colorbar=True, heatmap=False, + show=True, **kwargs ): @@ -547,6 +548,11 @@ def pca_decomposition( Also draws a colorbar for readability purpose. The heatmap is accessible using lax property and colorbar using uax property. + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -600,7 +606,11 @@ def pca_decomposition( # Fit and transform the visualizer (calls draw) visualizer.fit(X, y) visualizer.transform(X, y) - visualizer.finalize() + + if show: + visualizer.show() + else: + visualizer.finalize() # Returns the visualizer object. return visualizer From 425d9573e1d6e8027349b37ae5e9714290414ac4 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Tue, 7 Jan 2020 18:14:59 -0500 Subject: [PATCH 13/44] Resolve CI test failures by removing pegged numba and umap-learn versions (#1006) Resolves CI test failures and resolves #790 by removing pegged numba version and updates the umap-learn version. Also xfails the elbow locator test. --- .../test_elbow/test_locate_elbow.png | Bin 24206 -> 24205 bytes tests/requirements.txt | 4 ++-- tests/test_cluster/test_elbow.py | 15 ++++++++------- 3 files changed, 10 insertions(+), 9 deletions(-) diff --git a/tests/baseline_images/test_cluster/test_elbow/test_locate_elbow.png b/tests/baseline_images/test_cluster/test_elbow/test_locate_elbow.png index 1e5a22c5241ef5a792f98e0cc2da0d4b74352e38..f11ddd5c0219e236685d8267a2f838757f4752ea 100644 GIT binary patch delta 31 ncmeC%%hF5-h6S3%xdWGsgh{qW1~! delta 32 ocmeC(%h=4.5.2 nltk>=3.2 # spacy>=2.0.18 pandas>=0.20 -umap-learn==0.3 -numba==0.42 +umap-learn==0.3.9 # reminder to bump periodically! + diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py index c3431e531..0159960f3 100644 --- a/tests/test_cluster/test_elbow.py +++ b/tests/test_cluster/test_elbow.py @@ -306,6 +306,7 @@ def test_calinski_harabasz_metric(self): self.assert_images_similar(visualizer) assert_array_almost_equal(visualizer.k_scores_, expected) + @pytest.mark.xfail(IS_WINDOWS_OR_CONDA, reason="computation of k_scores_ varies by 2.867 max absolute difference") def test_locate_elbow(self): """ Test the addition of locate_elbow to an image @@ -326,17 +327,17 @@ def test_locate_elbow(self): assert visualizer.elbow_value_ == 3 expected = np.array( [ - 4286.4798481306625, - 12463.383743070379, - 8763.75791732466, - 6942.167328461612, - 5859.608884917707, + 4286.5, + 12463.4, + 8763.8, + 6939.3, + 5858.8, ] ) visualizer.finalize() - self.assert_images_similar(visualizer, windows_tol=2.2) - assert_array_almost_equal(visualizer.k_scores_, expected) + self.assert_images_similar(visualizer, tol=0.5, windows_tol=2.2) + assert_array_almost_equal(visualizer.k_scores_, expected, decimal=1) def test_no_knee(self): """ From ac823cb93ef7e54d4932605ef4fcc55a64ee9878 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Tue, 7 Jan 2020 20:06:41 -0500 Subject: [PATCH 14/44] Do not install tests subpackages with pip (#1007) The new module rules mean that directories without a __init__.py file can be found, which meant that the find_packages method was discovering tests subpackages. By adding the wildcard flag to excludes, we omit these packages from our builds and they will not be installed by pip or setup.py on the next release. Fixes #996 --- setup.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/setup.py b/setup.py index babb04d3e..d47d5f66e 100755 --- a/setup.py +++ b/setup.py @@ -77,14 +77,14 @@ ## Directories to ignore in find_packages EXCLUDES = ( - "tests", + "tests", "tests.*", "bin", - "docs", + "docs", "docs.*", "fixtures", "register", - "notebooks", - "examples", - "binder", + "notebooks", "notebooks.*", + "examples", "examples.*", + "binder", "binder.*", "paper", ) From 4f615fe01104f7ffea0b37ed0ff33fa08a8800b2 Mon Sep 17 00:00:00 2001 From: Piyush Gautam Date: Wed, 8 Jan 2020 08:21:31 +0530 Subject: [PATCH 15/44] RFECV quick method update (#995) * changes * changes * changes * changes to doc * Correct table rendering in docs * changes * changes * changes * changes to doc * Correct table rendering in docs * added images to docs * added images to docs * Minor changes to the api * Minor changes * added show=False to rfecv quick method Co-authored-by: pdamodaran --- 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zk<*DBx(+^gqB*&0-I~0mp0OPvui7Md%E-b$RW4X?M$tG8UT2eP&KRPnF5HoZ$a0OvV*)qlb5YtsL zT_wenC6X;SGG>z{I5cY;JKKHz`UgG7%waiX!?YE~2oW4=bEv-F70Z!&u9*F_$fe1@ zI%dEOwy+?)=K1Rc4p}1V2L4f|!Fq*sOC26FYKk&99_kc~$*gJ@UO!x?QmMpIZyFj~ zYU%V<}scZcLVLQNDwgzapv|h^3&zI*E7H-a5H0iHT@*4it(!HWk2Z%oK zE8D_0J#i!()k;15kAR59Q5%ZbOUZs~< zt1Lz+HL|zQ;wT=edhv_}egOV={eQ%an z>Sk`Tz<&72Dw*!!w#dl0hVBi>zg9D~rLUH~`NhD<=wAN94~+t6Y`s@1GK}wqt?M#} zKe2ao++Z>4yn8S-C3gS_CQX+Z*e~6uHC00_yW%x@q((i&EO-ThVmwS%mnhQGf6$xF-Is48SxZ`J}f*$O-QAHJD8G(7w>xELrcKC(Kh zM^&5`KQG*gM+DHh$?}9axPx@V(@Fkrlx_&pJCjPOSXf5T^r(bO5J+ z!7Lg^Z{yfQz{bzoqXBs^i42~mHTETkWiDPQ72?W4j!8*LLgLW33lXD^Y>4q9L_pUq z=p*I1KJJ^=*zm9xIO0guz8Y%|j?Mi`_ + Quick Method `rfecv() `_ + Models Classification, Regression + Workflow Model Selection + ================= ===================== + Recursive feature elimination (RFE) is a feature selection method that fits a model and removes the weakest feature (or features) until the specified number of features is reached. Features are ranked by the model's ``coef_`` or ``feature_importances_`` attributes, and by recursively eliminating a small number of features per loop, RFE attempts to eliminate dependencies and collinearity that may exist in the model. RFE requires a specified number of features to keep, however it is often not known in advance how many features are valid. To find the optimal number of features cross-validation is used with RFE to score different feature subsets and select the best scoring collection of features. The ``RFECV`` visualizer plots the number of features in the model along with their cross-validated test score and variability and visualizes the selected number of features. @@ -63,10 +70,30 @@ In this example we can see that 19 features were selected, though there doesn't .. seealso:: This visualizer is is based off of the visualization in the scikit-learn documentation: `recursive feature elimination with cross-validation `_. However, the Yellowbrick version does not use ``sklearn.feature_selection.RFECV`` but instead wraps ``sklearn.feature_selection.RFE`` models. The fitted model can be accessed on the visualizer using the ``viz.rfe_estimator_`` attribute, and in fact the visualizer acts as the fitted model when using ``predict()`` or ``score()``. +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method ``rfecv``. This method will build the ``RFECV`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. + +.. code:: python + + from sklearn.ensemble import RandomForestClassifier + from sklearn.model_selection import StratifiedKFold + + from yellowbrick.model_selection import rfecv + from yellowbrick.datasets import load_credit + + # Load classification dataset + X, y = load_credit() + + cv = StratifiedKFold(5) + visualizer = rfecv(RandomForestClassifier(), X=X, y=y, cv=cv, scoring='f1_weighted') + +.. image:: images/rfecv_quick_method.png + API Reference ------------- .. automodule:: yellowbrick.model_selection.rfecv - :members: RFECV + :members: RFECV, rfecv :undoc-members: :show-inheritance: diff --git a/tests/test_model_selection/test_rfecv.py b/tests/test_model_selection/test_rfecv.py index 3b27b975a..340d34130 100644 --- a/tests/test_model_selection/test_rfecv.py +++ b/tests/test_model_selection/test_rfecv.py @@ -133,7 +133,7 @@ def test_quick_method(self): model = LogisticRegression() X, y = self.dataset - viz = rfecv(model, X, y, step=2, cv=cv, scoring="f1_weighted") + viz = rfecv(model, X, y, step=2, cv=cv, scoring="f1_weighted", show=False) self.assert_images_similar(viz, remove_legend=True) diff --git a/yellowbrick/model_selection/rfecv.py b/yellowbrick/model_selection/rfecv.py index f98e65a2c..1386e4f5d 100644 --- a/yellowbrick/model_selection/rfecv.py +++ b/yellowbrick/model_selection/rfecv.py @@ -262,7 +262,7 @@ def finalize(self, **kwargs): ########################################################################## -def rfecv(model, X, y, ax=None, step=1, groups=None, cv=None, scoring=None, **kwargs): +def rfecv(model, X, y, ax=None, step=1, groups=None, cv=None, scoring=None, show=True, **kwargs): """ Performs recursive feature elimination with cross-validation to determine an optimal number of features for a model. Visualizes the feature subsets @@ -318,6 +318,11 @@ def rfecv(model, X, y, ax=None, step=1, groups=None, cv=None, scoring=None, **kw ``scorer(estimator, X, y)``. See scikit-learn model evaluation documentation for names of possible metrics. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. These arguments are @@ -330,10 +335,15 @@ def rfecv(model, X, y, ax=None, step=1, groups=None, cv=None, scoring=None, **kw Returns the fitted, finalized visualizer. """ # Initialize the visualizer - oz = RFECV(model, ax=ax, step=step, groups=groups, cv=cv, scoring=scoring) + oz = RFECV(model, ax=ax, step=step, groups=groups, cv=cv, scoring=scoring, show=show) # Fit and show the visualizer oz.fit(X, y) - oz.show(**kwargs) + if show: + oz.show() + else: + oz.finalize() + + # Return the visualizer object return oz From ec8c1ed6423b037e016bea6d8e456132a0dc1334 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Sun, 12 Jan 2020 15:03:54 -0500 Subject: [PATCH 16/44] January 2020 Advisory Board Minutes (#1009) Minutes for the January 2020 Advisory Board Meeting --- docs/governance/index.rst | 1 + docs/governance/minutes/2020-01-07.rst | 176 +++++++++++++++++++++++++ 2 files changed, 177 insertions(+) create mode 100644 docs/governance/minutes/2020-01-07.rst diff --git a/docs/governance/index.rst b/docs/governance/index.rst index a94daf5ef..a7e08b493 100644 --- a/docs/governance/index.rst +++ b/docs/governance/index.rst @@ -217,3 +217,4 @@ Board of Advisors Minutes minutes/2019-05-15.rst minutes/2019-09-09.rst + minutes/2020-01-07.rst diff --git a/docs/governance/minutes/2020-01-07.rst b/docs/governance/minutes/2020-01-07.rst new file mode 100644 index 000000000..06dd7af71 --- /dev/null +++ b/docs/governance/minutes/2020-01-07.rst @@ -0,0 +1,176 @@ +.. -*- mode: rst -*- + +January 7, 2020 +=============== + +Yellowbrick Advisory Board Meeting Held on January 7, 2020 from 2030-2230 EST via Video Conference Call. Rebecca Bilbro presiding as Chair, Benjamin Bengfort serving as Secretary and Edwin Schmierer as Treasurer. Minutes taken by Benjamin Bengfort. + +Attendees: Rebecca Bilbro, Benjamin Bengfort, Kristen McIntyre, Tony Ojeda, Larry Gray, Prema Roman, Edwin Schmierer, Nathan Danielsen, and Adam Morris. + +Agenda +------ + +A broad overview of the topics for discussion in the order they were presented: + +1. Welcome (Rebecca Bilbro) +2. Fall 2019 retrospective (Rebecca Bilbro) +3. Yellowbrick v1.2 milestone planning (Benjamin Bengfort) +4. Advisor dues and Yellowbrick Budget (Edwin Schmierer) +5. Officer nominations and election +6. Spring 2020 contributors and roles + +Votes and Resolutions +--------------------- + +Operating Budget +~~~~~~~~~~~~~~~~ + +The following operating budget is proposed for 2020. + +The board discussed a minor increase in operating costs from the previous year and acknowledged a 2020 budget of **$271.48** and **$30.17** advisor member dues per person. Because of the minimal change, the board is leaving the final budget open to any additional proposed expenses by January 14. + +Budget breakdown: + +============================================= ================ ========= + Description Frequency Total +============================================= ================ ========= +Name.com domain registration (scikit-yb.org) annually $17.98 +Read the Docs Gold Membership $10 monthly $120.00 +Stickers (StickerMule.com) annually $133.50 +Datasets hosting on S3 monthly (DDL) $10-15 +Cheatsheets annually (DDL) $70 +============================================= ================ ========= + + +Advisors to pay dues to Edwin via Venmo. Edwin will send his Venmo handle and QR code via YB Slack channel. Dues are payable by Feb 14, 2020. If any advisors would like to make changes or add to the budget, please contact Edwin by January 14 via email or Slack. Edwin will take suggestions to the board for discussion and vote by the end of January. + +A special thank you to District Data Labs for covering travel costs to PyCon, the printing of cheatsheets, and hosting our datasets on S3 for another year! + +Motion: any opposed to the current budget and leaving the budget open until the end of January. Moved by Edwin Shmierer, seconded by Benjamin Bengfort. + +*No opposition to the motion*. + +Officer Elections +~~~~~~~~~~~~~~~~~ + +During the first advisory board meeting of the year, officers are nominated and elected to manage Yellowbrick for the year. Current officers gave an overview of the role and responsibilities for Chair, Secretary, and Treasurer, respectively. The following nominations were proposed: + +Nominations for Chair: + +- Kristen McIntyre by Benjamin Bengfort + +Motion: a vote to elect Kristen McIntyre as 2020 Chair. Moved by Rebecca Bilbro, seconded by Larry Gray. + +*The motion was adopted unanimously*. + +Nominations for Secretary: + +- Larry Gray by Rebecca Bilbro + +Motion: a vote to elect Larry Gray as 2020 Secretary. Moved by Rebecca Bilbro, seconded by Prema Roman. + +*The motion was adopted unanimously*. + +Nominations for Treasurer: + +- Edwin Schmierer by Rebecca Bilbro + +Motion: a vote to elect Edwin Shmierer as 2020 Treasurer. Moved by Rebecca Bilbro, seconded by Kristen McIntyre. + +*The motion was adopted unanimously*. + +Kristen McIntyre and Larry Gray addressed the board expressing gratitude for the opportunity to serve and voicing their commitment to fulfilling the responsibilities to the best of their abilities. + +Fall 2019 Retrospective +----------------------- + +In traditional agile development, sprints are concluded with a retrospective to discuss what went well, what didn't go well, what were unexpected challenges, and how we can adapt to those challenges in the future. + +Accomplishments +~~~~~~~~~~~~~~~ + +- Semester has been a lot lighter than previous semesters, focused on quick methods. +- Fixing a bug with PCA visualizer (regression made with projection argument). +- Decision to move from ``poof()`` to ``show()`` -- we made a decision and executed on it (even in the face of a difficult situation). +- Test coverage has been catching things well in advance of problems (and before users experience them). +- Dependency set is small, which limits the number of problems we can have from them. +- Had a lot of contributors in the fall group (at least on the roster). +- 6 new contributors who opened PRs. +- 12 participants in the “burrito poll” made the API decision of the quick method signature. + + +Shoutouts +~~~~~~~~~ + +- Matt Harrison featured Yellowbrick in his machine learning book. +- Molly Morrison, Michael Chestnut, Michael Garod, Rohan Panda, George Krug, and Stephanie Miller all contributed to the quick methods development. +- Prema Roman has done a wonderful job this semester, stepped up and responding on GitHub, helping with new contributors, responding to questions. +- Guidance document made it easier and clearer about how to take a break for a semester. + +Challenges +~~~~~~~~~~ + +Potentially the biggest challenge Yellowbrick had this semester was a note from a user that the name of one of our methods was potentially a homophobic slur. Although the board considered the name to be one of the unique and charming features of Yellowbrick, we quickly decided to make a change to the method name, particularly as we were in the process of incrementing a major version number, which meant support for backward compatibility was not required. + +- Updated dependencies breaking CI (numba, umap-learn, sklearn, scipy) -- happened 2x +- Inconsistent behavior between miniconda and vanilla Python +- Jobs and other priorities have been swamping our ability to contribute to YB as much as we’d like to; the roles are still a bit new and we wish we could do more than we are +- There wasn’t a lot of activity from all of the contributors; perhaps we didn’t engage enough? Or maybe we want to have a range of issues available for different skill/experience levels? +- 7 quick methods are completed, 3 or 4 in review, 35 were assigned - most people who were assigned quick methods didn’t show up after the start of the semester. +- 10 new contributors who signed up but didn’t open a PR or participate much + +Semester and Roadmap +-------------------- + +v1.1 Status +~~~~~~~~~~~ + +The v1.1 release has not yet been finalized because the goal of updating all of the quick methods has not been completed. Because we've done the hard work of figuring out the API signature and documentation mechanism, the board feels that we should finish all the quick methods before releasing v1.1 *before the end of February*. + +In order to complete v1.1 the board will hold one or two hackathons (either in person or virtual) to complete the quick methods together and with food and drink. + +Spring 2020: v1.2 +~~~~~~~~~~~~~~~~~ + +The Spring 2020 goal is to have everyone implement and review one new visualizer. Implementing visualizers from scratch is much more fun than trying to improve current visualizers or fix bugs - so the focus of this semester is fun! We'll be looking at Probability Curves, Missing Values, Multimodal Visualizers and more, trying to have a flashy semester! + +Visualizer implementation and review assignments will be handed out at the beginning of the semester depending on preference. + +Additionally, the board will prepare for PyCon sprints by implementing cheatsheets and preparing blog posts and other materials for people to quickly get started with Yellowbrick, scikit-learn, and matplotlib. + +Summer 2020: v1.3 +~~~~~~~~~~~~~~~~~ + +It is likely that the Summer 2020 goal will be to implement visual pipelines. This is a research oriented task that doesn't have a clear path forward, so some planning before the semester is required. This is a major new feature to the libary. + +Spring 2020 Contributors +~~~~~~~~~~~~~~~~~~~~~~~~ + +Note: those wishing to sign up as core contributors must have previously successfully contributed to Yellowbrick by closing a PR. Yellowbrick is open to unassigned, non-core submissions from anyone at any time. + +===================== ================= ========== ======== +Name Role Visualizer Reviewer +===================== ================= ========== ======== +Rebecca Bilbro Maintainer yes yes +Benjamin Bengfort Core Contributor yes yes +Nathan Danielsen Maintainer yes yes +Kristen McIntyre Core Contributor yes yes +Larry Gray Core Contributor yes yes +Adam Morris Coordinator pair yes +Prema Roman Maintainer yes yes +===================== ================= ========== ======== + +Minutes +------- + +This meeting was primarily focused on electing the new board members and doing some initial planning for the year ahead. No additional topics were discussed. + +Action Items +------------ + +- Sign up for contributor roles and specify implement or review a visualizer (all) +- Review the 2020 budget and add any additional budget requests (all) +- Coordinate quick methods hackathon to finalize v1.1 (Kristen McIntyre) +- Assign visualizers and reviewers (Benjamin Bengfort) +- Update v1.1, v1.2, and v1.3 milestones on GitHub (Benjamin Bengfort & Larry Gray) +- Coordinate PyCon sprints with maintainers (Larry Gray and Prema Roman) From 1ba6774a257bc85768a990293790caf4c14a5653 Mon Sep 17 00:00:00 2001 From: mchestnut91 <40185521+mchestnut91@users.noreply.github.com> Date: Sun, 12 Jan 2020 16:23:11 -0500 Subject: [PATCH 17/44] Revised threshold quick method and test. Added documentation (#986) Revised threshold quick method and test, and added documentation. Move caution and notes into notes section inside docstring to fix sphinx rendering issues. Co-authored-by: Rebecca Bilbro --- docs/api/classifier/threshold.rst | 28 ++++++++++++++- tests/test_classifier/test_threshold.py | 2 +- yellowbrick/classifier/threshold.py | 45 ++++++++++++++++++++++--- yellowbrick/text/freqdist.py | 2 ++ 4 files changed, 70 insertions(+), 7 deletions(-) diff --git a/docs/api/classifier/threshold.rst b/docs/api/classifier/threshold.rst index 4b4f4faec..23454ca8d 100644 --- a/docs/api/classifier/threshold.rst +++ b/docs/api/classifier/threshold.rst @@ -7,6 +7,13 @@ Discrimination Threshold A visualization of precision, recall, f1 score, and queue rate with respect to the discrimination threshold of a binary classifier. The *discrimination threshold* is the probability or score at which the positive class is chosen over the negative class. Generally, this is set to 50% but the threshold can be adjusted to increase or decrease the sensitivity to false positives or to other application factors. +================= ================= +Visualizer `DiscriminationThreshold `_ +Quick Method `discrimination_threshold() `_ +Models Classification +Workflow Model evaluation +================= ================= + .. plot:: :context: close-figs :alt: Discrimination Threshold of a binary classifier @@ -42,11 +49,30 @@ Generally speaking, the threshold is balanced between cases and set to 0.5 or 50 In the figure above we see the visualizer tuned to look for the optimal F1 score, which is annotated as a threshold of 0.43. The model is run multiple times over multiple train/test splits in order to account for the variability of the model with respect to the metrics (shown as the fill area around the median curve). +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method ``discrimination_threshold``. This method will build the ``DiscriminationThreshold`` object with the associated arguments, fit it, then (optionally) immediately show it + +.. plot:: + :context: close-figs + :alt: discrimination_threshold on the occupancy dataset + + from yellowbrick.classifier.threshold import discrimination_threshold + from yellowbrick.datasets import load_occupancy + from sklearn.neighbors import KNeighborsClassifier + + #Load the classification dataset + X, y = load_occupancy() + + # Instantiate the visualizer with the classification model + model = KNeighborsClassifier(3) + + discrimination_threshold(model, X, y) API Reference ------------- .. automodule:: yellowbrick.classifier.threshold - :members: DiscriminationThreshold + :members: DiscriminationThreshold, discrimination_threshold :undoc-members: :show-inheritance: diff --git a/tests/test_classifier/test_threshold.py b/tests/test_classifier/test_threshold.py index 873a54495..19a052939 100644 --- a/tests/test_classifier/test_threshold.py +++ b/tests/test_classifier/test_threshold.py @@ -161,7 +161,7 @@ def test_quick_method(self): _, ax = plt.subplots() - discrimination_threshold(BernoulliNB(3), X, y, ax=ax, random_state=5) + discrimination_threshold(BernoulliNB(3), X, y, ax=ax, random_state=5, show=False) self.assert_images_similar(ax=ax, tol=10) @patch.object(DiscriminationThreshold, "draw", autospec=True) diff --git a/yellowbrick/classifier/threshold.py b/yellowbrick/classifier/threshold.py index 040cb06b4..6c23359a5 100644 --- a/yellowbrick/classifier/threshold.py +++ b/yellowbrick/classifier/threshold.py @@ -69,8 +69,6 @@ class DiscriminationThreshold(ModelVisualizer): review the data), given a tolerance for precision and recall or limiting the number of records to check (the queue rate). - .. caution:: This method only works for binary, probabilistic classifiers. - Parameters ---------- model : estimator @@ -165,6 +163,8 @@ class DiscriminationThreshold(ModelVisualizer): ``predict_proba`` method in order to be used with this class. A ``YellowbrickTypeError`` is raised otherwise. + .. caution:: This method only works for binary, probabilistic classifiers. + .. seealso:: For a thorough explanation of discrimination thresholds, see: `Visualizing Machine Learning Thresholds to Make Better Business @@ -495,7 +495,6 @@ def _check_exclude(self, val): # Quick Methods ########################################################################## - def discrimination_threshold( model, X, @@ -510,9 +509,10 @@ def discrimination_threshold( random_state=None, is_fitted="auto", force_model=False, + show=True, **kwargs ): - """Discrimination Threshold: + """Discrimination Threshold Visualizes how precision, recall, f1 score, and queue rate change as the discrimination threshold increases. For probabilistic, binary classifiers, @@ -603,9 +603,40 @@ def discrimination_threshold( will prevent an exception when the visualizer is initialized but may result in unexpected or unintended behavior. + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments passed to the visualizer base classes. + Notes + ----- + The term "discrimination threshold" is rare in the literature. Here, we + use it to mean the probability at which the positive class is selected + over the negative class in binary classification. + + Classification models must implement either a ``decision_function`` or + ``predict_proba`` method in order to be used with this class. A + ``YellowbrickTypeError`` is raised otherwise. + + .. seealso:: + For a thorough explanation of discrimination thresholds, see: + `Visualizing Machine Learning Thresholds to Make Better Business + Decisions + `_ + by Insight Data. + + Examples + -------- + >>> from yellowbrick.classifier.threshold import discrimination_threshold + >>> from sklearn.linear_model import LogisticRegression + >>> from yellowbrick.datasets import load_occupancy + >>> X, y = load_occupancy() + >>> model = LogisticRegression(multi_class="auto", solver="liblinear") + >>> discrimination_threshold(model, X, y) + Returns ------- viz : DiscriminationThreshold @@ -629,7 +660,11 @@ def discrimination_threshold( # Fit and transform the visualizer (calls draw) visualizer.fit(X, y) - visualizer.finalize() + + if show: + visualizer.show() + else: + visualizer.finalize() # Return the visualizer return visualizer diff --git a/yellowbrick/text/freqdist.py b/yellowbrick/text/freqdist.py index 4c75b45d5..456e77113 100644 --- a/yellowbrick/text/freqdist.py +++ b/yellowbrick/text/freqdist.py @@ -171,6 +171,8 @@ def fit(self, X, y=None): y : ndarray or DataFrame of shape n Labels for the documents for conditional frequency distribution. + Notes + ----- .. note:: Text documents must be vectorized before ``fit()``. """ From 863fe6795e198abdc4f97f3a5af873cb7b776df8 Mon Sep 17 00:00:00 2001 From: Emma Kwiecinska Date: Tue, 14 Jan 2020 16:26:57 +0000 Subject: [PATCH 18/44] Parsing raw text with PosTagVisualizer (#988) This PR introduces a new feature discussed in issue #771 which allows the user to optionally parse their raw text documents directly using the PosTagVisualiser. PosTagVisualiser now accepts a parser argument, which allow the user to specify if they require parsing of raw text (their documents are untagged) and also which library and tagset to use Unit tests for both spacy and nltk variants and also for the word and wordpunct taggers in nltk. --- docs/api/text/postag.rst | 56 +++++- .../test_postag/test_nltk_word_raw.png | Bin 0 -> 3573 bytes .../test_postag/test_nltk_wordpunct_raw.png | Bin 0 -> 3578 bytes .../test_text/test_postag/test_spacy_raw.png | Bin 0 -> 3503 bytes tests/test_text/test_postag.py | 36 ++++ yellowbrick/text/postag.py | 184 +++++++++++++++--- 6 files changed, 251 insertions(+), 25 deletions(-) create mode 100644 tests/baseline_images/test_text/test_postag/test_nltk_word_raw.png create mode 100644 tests/baseline_images/test_text/test_postag/test_nltk_wordpunct_raw.png create mode 100644 tests/baseline_images/test_text/test_postag/test_spacy_raw.png diff --git a/docs/api/text/postag.rst b/docs/api/text/postag.rst index 5f4c080b3..f66c8cd1e 100644 --- a/docs/api/text/postag.rst +++ b/docs/api/text/postag.rst @@ -5,10 +5,10 @@ PosTag Visualization Parts of speech (e.g. verbs, nouns, prepositions, adjectives) indicate how a word is functioning within the context of a sentence. In English as in many other languages, a single word can function in multiple ways. Part-of-speech tagging lets us encode information not only about a word’s definition, but also its use in context (for example the words “ship” and “shop” can be either a verb or a noun, depending on the context). -The ``PosTagVisualizer`` is intended to support grammar-based feature extraction techniques for machine learning workflows that require natural language processing. The visualizer reads in a corpus that has already been sentence- and word-segmented, and tagged, creating a bar chart to visualize the relative proportions of different parts-of-speech in a corpus. +The ``PosTagVisualizer`` is intended to support grammar-based feature extraction techniques for machine learning workflows that require natural language processing. The visualizer can either read in a corpus that has already been sentence- and word-segmented, and tagged, or perform this tagging automatically by specifying the parser to use (nltk or spacy). The visualizer creates a bar chart to visualize the relative proportions of different parts-of-speech in a corpus. .. note:: - The ``PosTagVisualizer`` currently works with both Penn-Treebank (e.g. via NLTK) and Universal Dependencies (e.g. via SpaCy)-tagged corpora, but expects corpora that have already been tagged, and which take the form of a list of (document) lists of (sentence) lists of ``(token, tag)`` tuples, as in the example below. + The ``PosTagVisualizer`` currently works with both Penn-Treebank (e.g. via NLTK) and Universal Dependencies (e.g. via SpaCy)-tagged corpora. This expects either raw text, or corpora that have already been tagged which take the form of a list of (document) lists of (sentence) lists of ``(token, tag)`` tuples, as in the example below. Penn Treebank Tags ------------------ @@ -223,6 +223,58 @@ Libraries like SpaCy use tags from the Universal Dependencies (UD) framework. Th | WRB | Wh-adverb | | | +-------------------+------------------------------------------+----------------------+--------------------------+ +Parsing raw text automatically +--------------------------- + +The ``PosTagVisualizer`` can also be used with untagged text by using the ``parse`` keyword on instantiation. The keyword +to parse indicates which natural language processing library to use. To use ``spacy``: + + +.. code:: python + + untagged_speech = u'Whose woods these are I think I know' + + # Create the visualizer, fit, score, and show it + viz = PosTagVisualizer(parser='spacy') + viz.fit(untagged_speech) + viz.show() + + +Or, using the ``nltk`` parser. + +.. code:: python + + untagged_speech = u'Whose woods these are I think I know' + + # Create the visualizer, fit, score, and show it + viz = PosTagVisualizer(parser='nltk') + viz.fit(untagged_speech) + viz.show() + +.. note:: + To use either of these parsers, either `nltk` or `spacy` must already be installed in your environment. + +You can also change the tagger used. For example, using `nltk` you can select either `word` (default): + +.. code:: python + + untagged_speech = u'Whose woods these are I think I know' + + # Create the visualizer, fit, score, and show it + viz = PosTagVisualizer(parser='nltk_word') + viz.fit(untagged_speech) + viz.show() + +Or using `wordpunct`. + +.. code:: python + + untagged_speech = u'Whose woods these are I think I know' + + # Create the visualizer, fit, score, and show it + viz = PosTagVisualizer(parser='nltk_wordpunct') + viz.fit(untagged_speech) + viz.show() API Reference ------------- diff --git a/tests/baseline_images/test_text/test_postag/test_nltk_word_raw.png b/tests/baseline_images/test_text/test_postag/test_nltk_word_raw.png new file mode 100644 index 0000000000000000000000000000000000000000..50dd117ffe52832c5ceb5a963dce7529c4aca221 GIT binary patch literal 3573 zcmeAS@N?(olHy`uVBq!ia0y~yU{+vYU{d2?1B$GUdv_2>u@pObhHwBu4M$1`kk47* z5n0T@z@G@hj4SMyX95K+OI#yLg7ec#$`gxH8FCX#3UcyGax#+?%2JDpGxPHljP(rk zjC2$-N=gc>^z{)6^zw_+^(`8#*8*J-{ zqoc$jwy&e7iK_E!vEBGFs4Y(k>M4ztC-?afbqpSnCE zowaPwzZ~K4&o<|U85kTMEMF=Pbf+F4BNGEd3JV7V!vsMEptxHDP*ieMkcN;`1LKGMqqs?13Dsll}bR=futXZT8ydm@zP< zPnH&7U{FwYU|?wIVPIrnFk)h1U^u}sDmWS-qlsZOBT%JeI8*hKHDSx*y@kjBTs!$Z zaWf;sIc0N1#q*zqo8jDs^mg{cY4$rlSKP>X@TiEH+!_j0#>Lp2=i_fXQ(N%-F z?41S%TDE4cdFwMgzt}x@xAU`mzdse7h+$~xBBu@oxt(F##m$GC6F1A>sk8ys#yQqV zxdLl*Y=4`$g>adioxIua)0clAPGe^H|CEiG3j04h%Y&w>jm70R-c-lO<<bwBe|P`1cJledY5AMg-QTyJtt)wbYr{>3=QGI(6)Lwu zz;0wXw;!o+sk1=@^31C?=RaTa{&4m6_4Ugu=Pmc1u9y10=KsT#8*f&Je>1bQ|M~jx zgzse(q{C`sa|5R8# zsofuQ@9vL1cB>bkduft;K2o;SZ+T|*xmPQSt$gp>t=_77PV0~S^QUX-etpS|S{rux z>}>PR<@3y$Qo3d2<$=va|J}Q9e_Y7MaG-wfym@}BuV&r4e?Puzs5tYAFCnvMFdzZFnGH9xvXu@pObhHwBu4M$1`kk47* z5n0T@z@G@hj4SMyX95K+OI#yLg7ec#$`gxH8FCX#3UcyGax#+?%2JDpGxPHljP(rk zjC2$-N=gc>^z{)6^zw_+^(`8#*8*J-?&;zfQZeW4oxNG1krE9LYt_`PT)Pe?z1k>{ zqoc$jcCVvpLiGlN=?hxFD{xdPYqA^^F>7iK_E!vEBGH@ZZ^+Xu&>eVvxBsN)n*JZ( z>Tme;d6VC{ta;BXnHU&2{%rXIqz)WabYNg;=wV=FU@&50VPH7HA;7?(pgbx_Mu4oD z9@p?+Mo!M}vyK1l+qb>fGcg>{?CbAeel^SV?Ynn+dr!?&buc?&bN;f;dGGa%3z|p5{RIQHARJ~+P*b;92xbNrM#pe&FF*D3@&vO`bb>HT=Dn`9| z%h`WBKYMq#(j-NXfx*5{T41=>$*-4+Gwi#6d-~&jQ`g`BXOm;bQ1EdFBN=rt?&b?9 zWFO>I{dskF!cPADSZO11h8sLIP60q~HGF>Y^V(g-;`ej+NC2%|@QA`>4GO6nF8=)A z+RxtG{W;@C4#SNSJihQh$aJn{E&KVlvv!q5^+44Ryz!_8Hv{e3#2++O?JF(6@upf` zzwIpBgPdaA4TBmkF^1>Y9*4hgJ6l)r`qqY<49{mGrPu8Vj5!^^Tvw-Z-|81AUjN7= z*?+Hz)#2IO(}nxX*4_VRX56s(057fDJK(}7Z7-X zUcRsH%fEib^S5{YdcAVJe7($f)4X`QzuA92KmIu3`QM_--&d}`-fZ=1j^E{HbFKzH zFPUZbd`+L#^2=YoZjOq6wC2!u3;p!3mzVor&e}RF@7|u7wx9P1Do8*1@S$MV^U7QI z?)AiHF)-A;{PCkAYHir%ckkX^T0JKc++P17KkN$!>h?>{+~4ofpXljx)0+VZJYD@< J);T3K0RZd-)3yKr literal 0 HcmV?d00001 diff --git a/tests/baseline_images/test_text/test_postag/test_spacy_raw.png b/tests/baseline_images/test_text/test_postag/test_spacy_raw.png new file mode 100644 index 0000000000000000000000000000000000000000..c002661e5464f28b3141f995fd851f257b894a71 GIT binary patch literal 3503 zcmeAS@N?(olHy`uVBq!ia0y~yU{+vYU{d2?1B$GUdv_2>u@pObhHwBu4M$1`kk47* z5n0T@z@G@hj4SMyX95K+OI#yLg7ec#$`gxH8FCX#3UcyGax#+?%2JDpGxPHljP(rk zjC2$-N=gc>^z{)6^zw_+^(`8#*8*L@l#C*t{C1_-Ps`Un{rzOd`Se=m z^Zf^;&rNT47W-^-UYLQw;lb>S%nS?-ubmr!&Xr_jVqi#N;b35xAgBP8bQ={UBS0ei zw2lTmua`s->?c0zKrW4l_K)wKXE1kPg-sREGC}jv zh#$=f^ehFYEoMJ({lVXFKf+($ySV%!uu#%_%ErRLaDqdCfk8pp0a!ZqFfcMO7%^cJ zeEh#!D(2DC+v$tdef;HY@9GPVp1KfcH1+sah^TYTBv`8>PX zbZKIOWw7{wQ1sEwou@A{HynGtw%z%BeaWx17e5&eTpcc{g!nK9rKbm*`QIP==eeu+ z|K$sx86Q{`(6)*}R7NwOzF=gS{pk7k{XO#x{+7Hy4$SHgx_2<*%-VEI8#J>>$DN3pd#h?0Z^Y%&ECR){gtoXvhu+Cse)%ZrZ=! z{_JO)wPE7*2N)OWXz|rl+TWnbNBYuGZcUd{P)%P5yr@_g9xkW_*sF#sCDK Lu6{1-oD!M Date: Thu, 6 Feb 2020 21:53:38 -0500 Subject: [PATCH 19/44] precision_recall_curve quick method updates (#1015) Refactors the precision_recall_curve quick method to the new quick method signature and verifies that it works correctly. Related to #960 --- docs/api/classifier/prcurve.rst | 34 +++++- docs/api/text/postag.rst | 2 +- .../test_prcurve/test_binary_decision.png | Bin 8731 -> 8731 bytes .../test_prcurve/test_binary_probability.png | Bin 8127 -> 8126 bytes .../test_binary_probability_decision.png | Bin 33640 -> 33640 bytes .../test_custom_iso_f1_scores.png | Bin 27459 -> 27458 bytes .../test_prcurve/test_multiclass_decision.png | Bin 16136 -> 16136 bytes .../test_multiclass_probability.png | Bin 39802 -> 39802 bytes ...lticlass_probability_with_class_labels.png | Bin 35183 -> 35183 bytes .../test_prcurve/test_pandas_integration.png | Bin 27462 -> 27462 bytes .../test_prcurve/test_quick_method.png | Bin 27381 -> 27416 bytes .../test_quick_method_with_test_set.png | Bin 6704 -> 6703 bytes tests/requirements.txt | 2 +- tests/test_classifier/test_prcurve.py | 10 +- yellowbrick/classifier/prcurve.py | 97 +++++++----------- 15 files changed, 75 insertions(+), 70 deletions(-) diff --git a/docs/api/classifier/prcurve.rst b/docs/api/classifier/prcurve.rst index 037025caa..d456aa688 100644 --- a/docs/api/classifier/prcurve.rst +++ b/docs/api/classifier/prcurve.rst @@ -11,6 +11,14 @@ under the curve represents both high recall and precision, the best case scenario for a classifier, showing a model that returns accurate results for the majority of classes it selects. +================= ============================== +Visualizer `PrecisionRecallCurve `_ +Quick Method `precision_recall_curve() `_ +Models Classification +Workflow Model evaluation +================= ============================== + + Binary Classification --------------------- @@ -98,10 +106,34 @@ A more complex Precision-Recall curve can be computed, however, displaying the e .. seealso:: `Scikit-Learn: Model Selection with Precision Recall Curves `_ + +Quick Method +------------ + +Similar functionality as above can be achieved in one line using the associated quick method, ``precision_recall_curve``. This method will instantiate and fit a ``PrecisionRecallCurve`` visualizer on the training data, then will score it on the optionally provided test data (or the training data if it is not provided). + +.. plot:: + :context: close-figs + :alt: precision_recall_curve quick method with binary classification + + from sklearn.naive_bayes import BernoulliNB + from sklearn.model_selection import train_test_split as tts + from yellowbrick.classifier import precision_recall_curve + from yellowbrick.datasets import load_spam + + # Load the dataset and split into train/test splits + X, y = load_spam() + + X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, shuffle=True) + + # Create the visualizer, fit, score, and show it + viz = precision_recall_curve(BernoulliNB(), X_train, y_train, X_test, y_test) + + API Reference ------------- .. automodule:: yellowbrick.classifier.prcurve - :members: PrecisionRecallCurve + :members: PrecisionRecallCurve, precision_recall_curve :undoc-members: :show-inheritance: diff --git a/docs/api/text/postag.rst b/docs/api/text/postag.rst index f66c8cd1e..9af40e8dd 100644 --- a/docs/api/text/postag.rst +++ b/docs/api/text/postag.rst @@ -224,7 +224,7 @@ Libraries like SpaCy use tags from the Universal Dependencies (UD) framework. Th +-------------------+------------------------------------------+----------------------+--------------------------+ Parsing raw text automatically ---------------------------- +------------------------------ The ``PosTagVisualizer`` can also be used with untagged text by using the ``parse`` keyword on instantiation. The keyword to parse indicates which natural language processing library to use. 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index 44826b490..40e98b490 100644 --- a/tests/requirements.txt +++ b/tests/requirements.txt @@ -15,6 +15,6 @@ coverage>=4.5.2 # Optional Testing Dependencies nltk>=3.2 # spacy>=2.0.18 -pandas>=0.20 +pandas>=0.20,<1 umap-learn==0.3.9 # reminder to bump periodically! diff --git a/tests/test_classifier/test_prcurve.py b/tests/test_classifier/test_prcurve.py index 502578e24..0efc859c1 100644 --- a/tests/test_classifier/test_prcurve.py +++ b/tests/test_classifier/test_prcurve.py @@ -340,22 +340,21 @@ def test_quick_method(self): Test the precision_recall_curve quick method with numpy arrays. """ X, y = load_occupancy(return_dataset=True).to_numpy() - - model = DecisionTreeClassifier(random_state=14) + model = DecisionTreeClassifier(random_state=23, max_depth=2) oz = precision_recall_curve( model, X, y, - per_class=True, + per_class=False, micro=True, fill_area=False, iso_f1_curves=True, ap_score=False, - random_state=2, + show=False ) - assert isinstance(oz, PrecisionRecallCurve) + assert isinstance(oz, PrecisionRecallCurve) self.assert_images_similar(oz) @pytest.mark.skipif(pd is None, reason="test requires pandas") @@ -440,7 +439,6 @@ def test_quick_method_with_test_set(self): y_train, X_test, y_test, - random_state=7, ) self.assert_images_similar(viz) diff --git a/yellowbrick/classifier/prcurve.py b/yellowbrick/classifier/prcurve.py index 96d8a7cbd..b6c8e5522 100644 --- a/yellowbrick/classifier/prcurve.py +++ b/yellowbrick/classifier/prcurve.py @@ -23,7 +23,6 @@ from sklearn.multiclass import OneVsRestClassifier from sklearn.utils.multiclass import type_of_target from sklearn.metrics import average_precision_score -from sklearn.model_selection import train_test_split as tts from sklearn.metrics import precision_recall_curve as sk_precision_recall_curve from yellowbrick.exceptions import ModelError, NotFitted @@ -434,13 +433,10 @@ def _get_y_scores(self, X): def precision_recall_curve( model, - X, - y, + X_train, + y_train, X_test=None, y_test=None, - train_size=0.8, - random_state=None, - shuffle=True, ax=None, classes=None, encoder=None, @@ -454,6 +450,7 @@ def precision_recall_curve( line_opacity=0.8, is_fitted="auto", force_model=False, + show=True, **kwargs ): """Precision-Recall Curve @@ -474,41 +471,27 @@ def precision_recall_curve( fitted, it is fit when the visualizer is fitted, unless otherwise specified by ``is_fitted``. - X : ndarray or DataFrame of shape n x m + X_train : ndarray or DataFrame of shape n x m A feature array of n instances with m features the model is trained on. - This array will be split into train and test splits if X_test is not specified. + Used to fit the visualizer and also to score the visualizer if test splits are + not directly specified. - y : ndarray or Series of length n - An array or series of target or class values. This vector will be split - into train and test splits if y_test is not specified. + y_train : ndarray or Series of length n + An array or series of target or class values. Used to fit the visualizer and + also to score the visualizer if test splits are not specified. - X_test : ndarray or DataFrame of shape n x m + X_test : ndarray or DataFrame of shape n x m, default: None An optional feature array of n instances with m features that the model - is tested on if specified, using X as the training data. Otherwise - X will be split into train and test splits. + is scored on if specified, using X_train as the training data. - y_test : ndarray or Series of length n - An array or series of target or class values that serve as actual labels for - X_test. If not specified, y will be split into test and train along with X. + y_test : ndarray or Series of length n, default: None + An optional array or series of target or class values that serve as actual + labels for X_test for scoring purposes. ax : matplotlib Axes, default: None The axes to plot the figure on. If not specified the current axes will be used (or generated if required). - train_size : float or int, default=0.8 - If float, should be between 0.0 and 1.0 and represent the proportion - of the dataset to include in the train split. If int, represents the - absolute number of train samples. Used if X_test and y_test not specified. - - random_state : int, RandomState, or None, optional - If int, random_state is the seed used by the random number generator; - If RandomState instance, random_state is the random number generator; - If None, the random number generator is the RandomState instance used - by np.random. - - shuffle : bool, default=True - Whether or not to shuffle the data before splitting. - classes : list of str, defult: None The class labels to use for the legend ordered by the index of the sorted classes discovered in the ``fit()`` method. Specifying classes in this @@ -569,6 +552,11 @@ def precision_recall_curve( will prevent an exception when the visualizer is initialized but may result in unexpected or unintended behavior. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments passed to the visualizer base classes. @@ -576,34 +564,7 @@ def precision_recall_curve( ------- viz : PrecisionRecallCurve Returns the visualizer that generates the curve visualization. - - Notes - ----- - Data is split using ``sklearn.model_selection.train_test_split`` before - computing the Precision-Recall curve. Splitting options such as train_size, - random_state, and shuffle are specified. Note that splits are not stratified, - if required, it is recommended to use the base class. """ - - if (X_test is None) and (y_test is None): - # Create train and test splits to validate the model - X_train, X_test, y_train, y_test = tts( - X, y, train_size=train_size, random_state=random_state, shuffle=shuffle - ) - elif any( - [ - ((X_test is not None) and (y_test is None)), - ((X_test is None) and (y_test is not None)), - ] - ): - # exception handling in case of missing X_test or y_test - raise YellowbrickValueError( - "both X_test and y_test are required if one is specified" - ) - - else: - X_train, y_train = X, y - # Instantiate the visualizer viz = PRCurve( model, @@ -623,10 +584,24 @@ def precision_recall_curve( **kwargs ) - # Fit and transform the visualizer + # Fit the visualizer viz.fit(X_train, y_train) - viz.score(X_test, y_test) - viz.finalize() + + # Score the visualizer + if X_test is not None and y_test is not None: + viz.score(X_test, y_test) + elif X_test is not None or y_test is not None: + raise YellowbrickValueError( + "both X_test and y_test are required if one is specified" + ) + else: + viz.score(X_train, y_train) + + # Draw the final visualization + if show: + viz.show() + else: + viz.finalize() # Return the visualizer return viz From c966a1d70dbc2111e3761e73053afce96ba553bb Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Fri, 7 Feb 2020 16:10:20 -0500 Subject: [PATCH 20/44] feature_importances quick method (#1017) Refactors the feature_importances quick method to the new quick method signature and verifies that it works correctly --- docs/api/cluster/silhouette.rst | 4 +++ docs/api/model_selection/importances.rst | 35 +++++++++++++++++-- .../test_model_selection/test_importances.py | 2 +- yellowbrick/model_selection/importances.py | 12 ++++++- 4 files changed, 48 insertions(+), 5 deletions(-) diff --git a/docs/api/cluster/silhouette.rst b/docs/api/cluster/silhouette.rst index c13decd2a..232da7cec 100644 --- a/docs/api/cluster/silhouette.rst +++ b/docs/api/cluster/silhouette.rst @@ -39,6 +39,10 @@ Examples and demo visualizer.fit(X) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure + +Quick Method +------------ + The same functionality above can be achieved with the associated quick method `silhouette_visualizer`. This method will build the Silhouette Visualizer object with the associated arguments, fit it, then (optionally) immediately show it. .. plot:: diff --git a/docs/api/model_selection/importances.rst b/docs/api/model_selection/importances.rst index e1e320748..325bf337c 100644 --- a/docs/api/model_selection/importances.rst +++ b/docs/api/model_selection/importances.rst @@ -16,8 +16,16 @@ other. In scikit-learn, Decision Tree models and ensembles of trees such as Random Forest, Gradient Boosting, and Ada Boost provide a ``feature_importances_`` attribute when fitted. The Yellowbrick ``FeatureImportances`` visualizer utilizes this attribute to rank and plot -relative importances. Let's start with an example; first load a -classification dataset. +relative importances. + +================= ============================== +Visualizer :class:`~yellowbrick.model_selection.importances.FeatureImportances` +Quick Method :func:`~yellowbrick.model_selection.importances.feature_importances` +Models Classification, Regression +Workflow Model selection, feature selection +================= ============================== + +Let's start with an example; first load a classification dataset. Then we can create a new figure (this is optional, if an ``Axes`` isn't specified, Yellowbrick will use the current @@ -127,10 +135,31 @@ In both cases, because the coefficient may be negative (indicating a strong nega This method may also be used for instances; but generally there are very many instances relative to the number models being compared. Instead a heatmap grid is a better choice to inspect the influence of features on individual instances. Here the grid is constructed such that the x-axis represents individual features, and the y-axis represents individual instances. The color of each cell (an instance, feature pair) represents the magnitude of the product of the instance value with the feature's coefficient for a single model. Visual inspection of this diagnostic may reveal a set of instances for which one feature is more predictive than another; or other types of regions of information in the model itself. +Quick Method +------------ + +The same functionality above can be achieved with the associated quick method `feature_importances`. This method will build the ``FeatureImportances`` object with the associated arguments, fit it, then (optionally) immediately show it. + +.. plot:: + :context: close-figs + :alt: feature_importances on the occupancy dataset + + from sklearn.ensemble import AdaBoostClassifier + from yellowbrick.datasets import load_occupancy + from yellowbrick.model_selection import feature_importances + + # Load dataset + X, y = load_occupancy() + + # Use the quick method and immediately show the figure + feature_importances(AdaBoostClassifier(), X, y) + + + API Reference ------------- .. automodule:: yellowbrick.model_selection.importances - :members: FeatureImportances + :members: FeatureImportances, feature_importances :undoc-members: :show-inheritance: diff --git a/tests/test_model_selection/test_importances.py b/tests/test_model_selection/test_importances.py index c829e926e..a556edcbf 100644 --- a/tests/test_model_selection/test_importances.py +++ b/tests/test_model_selection/test_importances.py @@ -105,7 +105,7 @@ def test_integration_quick_method(self): ax = fig.add_subplot() clf = RandomForestClassifier(random_state=42) - g = feature_importances(clf, X, y, ax=ax) + g = feature_importances(clf, X, y, ax=ax, show=False) # Appveyor and Linux conda non-text-based differences self.assert_images_similar(g, tol=15.0) diff --git a/yellowbrick/model_selection/importances.py b/yellowbrick/model_selection/importances.py index aa0da6067..c0221e334 100644 --- a/yellowbrick/model_selection/importances.py +++ b/yellowbrick/model_selection/importances.py @@ -366,6 +366,7 @@ def feature_importances( colors=None, colormap=None, is_fitted="auto", + show=True, **kwargs ): """Quick Method: @@ -426,6 +427,11 @@ def feature_importances( modified. If 'auto' (default), a helper method will check if the estimator is fitted before fitting it again. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -452,7 +458,11 @@ def feature_importances( # Fit and transform the visualizer (calls draw) visualizer.fit(X, y) - visualizer.finalize() + + if show: + visualizer.show() + else: + visualizer.finalize() # Return the visualizer return visualizer From 6a2a685372a2d351c02bcacd85d0960fa01bbcf2 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Fri, 7 Feb 2020 17:15:01 -0500 Subject: [PATCH 21/44] Make rank2d quick method prime time (#1018) Refactors the rank2d quick method to the new quick method signature and verifies that it works correctly. --- docs/api/features/rankd.rst | 26 +++++- .../test_rankd/test_rank1d_integrated.png | Bin 0 -> 3646 bytes .../test_rank1d_integrated_numpy.png | Bin 3645 -> 3645 bytes .../test_rank1d_integrated_pandas.png | Bin 3645 -> 3645 bytes .../test_rankd/test_rank1d_orientation.png | Bin 3959 -> 3959 bytes .../test_rankd/test_rank1d_random.png | Bin 0 -> 3988 bytes .../test_rankd/test_rank1d_shapiro.png | Bin 3846 -> 3846 bytes .../test_rankd/test_rank2d_covariance.png | Bin 11470 -> 11470 bytes .../test_rankd/test_rank2d_integrated.png | Bin 0 -> 11415 bytes .../test_rank2d_integrated_numpy.png | Bin 11414 -> 11414 bytes .../test_rank2d_integrated_pandas.png | Bin 11414 -> 11414 bytes .../test_rankd/test_rank2d_kendalltau.png | Bin 11704 -> 11704 bytes .../test_rankd/test_rank2d_pearson.png | Bin 11689 -> 11689 bytes .../test_rankd/test_rank2d_quick_method.png | Bin 0 -> 11402 bytes .../test_rankd/test_rank2d_random.png | Bin 0 -> 11812 bytes .../test_rankd/test_rank2d_spearman.png | Bin 11682 -> 11682 bytes tests/test_features/test_rankd.py | 14 ++++ yellowbrick/features/rankd.py | 79 +++++++++++------- 18 files changed, 86 insertions(+), 33 deletions(-) create mode 100644 tests/baseline_images/test_features/test_rankd/test_rank1d_integrated.png create mode 100644 tests/baseline_images/test_features/test_rankd/test_rank1d_random.png create mode 100644 tests/baseline_images/test_features/test_rankd/test_rank2d_integrated.png create mode 100644 tests/baseline_images/test_features/test_rankd/test_rank2d_quick_method.png create mode 100644 tests/baseline_images/test_features/test_rankd/test_rank2d_random.png diff --git a/docs/api/features/rankd.rst b/docs/api/features/rankd.rst index 43e84e824..e47e593e7 100644 --- a/docs/api/features/rankd.rst +++ b/docs/api/features/rankd.rst @@ -7,6 +7,13 @@ Rank Features In this example, we'll use the credit default data set from the UCI Machine Learning repository to rank features. The code below creates our instance matrix and target vector. +================= ============================== +Visualizers `Rank1D `_, `Rank2D `_ +Quick Methods `rank2d() `_ +Models General Linear Models +Workflow Feature engineering and model selection +================= ============================== + Rank 1D ------- @@ -74,11 +81,28 @@ Alternatively, we can utilize the covariance ranking algorithm, which attempts t visualizer.transform(X) # Transform the data visualizer.show() # Finalize and render the figure +Quick Methods +------------- + +Similar functionality as above can be achieved in one line using the associated quick method, ``rank2d``. This method will instantiate and fit a ``Rank2D`` visualizer on the dataset and immediately show it. + +.. plot:: + :context: close-figs + :alt: rank2d quick method on credit dataset with pearson algorithm + + from yellowbrick.datasets import load_credit + from yellowbrick.features import rank2d + + # Load the credit dataset + X, _ = load_credit() + + oz = rank2d(X) + API Reference ------------- .. automodule:: yellowbrick.features.rankd - :members: Rank1D, Rank2D + :members: Rank1D, Rank2D, rank2d :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_features/test_rankd/test_rank1d_integrated.png b/tests/baseline_images/test_features/test_rankd/test_rank1d_integrated.png new file mode 100644 index 0000000000000000000000000000000000000000..25fd1774831197852a17bf007c382fddedae64e5 GIT binary patch literal 3646 zcmeAS@N?(olHy`uVBq!ia0y~yU{+vYU{d2?1B$GUdv_2>u@pObhHwBu4M$1`kk47* z5n0T@z@G@hj4SMyX95K+OI#yLg7ec#$`gxH8FCX#3UcyGax#+?%2JDpGxPHljP(rk 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see #892", + ) + def test_rank2d_quick_method(self): + """ + Test Rank2D quick method + """ + X, y = load_occupancy() + oz = rank2d(X, y, algorithm="spearman", colormap="RdYlGn_r") + + assert isinstance(oz, Rank2D) + self.assert_images_similar(oz, tol=0.1) \ No newline at end of file diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 81c655d1f..804897934 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -359,8 +359,8 @@ class Rank2D(RankDBase): will be used (or generated if required). algorithm : str, default: 'pearson' - The ranking algorithm to use, one of: - 'pearson', 'covariance', 'spearman', 'kendalltau'. + The ranking algorithm to use, one of: 'pearson', 'covariance', 'spearman', + or 'kendalltau'. features : list A list of feature names to use. @@ -369,12 +369,11 @@ class Rank2D(RankDBase): colormap : string or cmap, default: 'RdBu_r' optional string or matplotlib cmap to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. + Use either color to colorize the lines on a per class basis or colormap to + color them on a continuous scale. show_feature_names : boolean, default: True - If True, the feature names are used to label the axis ticks in the - plot. + If True, the feature names are used to label the axis ticks in the plot. kwargs : dict Keyword arguments that are passed to the base class and may influence @@ -547,52 +546,62 @@ def rank2d( ax=None, algorithm="pearson", features=None, - show_feature_names=True, colormap="RdBu_r", + show_feature_names=True, + show=True, **kwargs ): - """Displays pairwise comparisons of features with the algorithm and ranks - them in a lower-left triangle heatmap plot. + """Rank2D quick method - This helper function is a quick wrapper to utilize the Rank2D Visualizer - (Transformer) for one-off analysis. + Rank2D performs pairwise comparisons of each feature in the data set with + a specific metric or algorithm (e.g. Pearson correlation) then returns + them ranked as a lower left triangle diagram. Parameters ---------- X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features + A matrix of n instances with m features to perform the pairwise compairsons on. - y : ndarray or Series of length n - An array or series of target or class values + y : ndarray or Series of length n, default: None + An array or series of target or class values, optional (not used). - ax : matplotlib axes - the axis to plot the figure on. + ax : matplotlib Axes, default: None + The axis to plot the figure on. If None is passed in the current axes + will be used (or generated if required). - algorithm : one of {pearson, covariance, spearman, kendalltau} - the ranking algorithm to use, default is Pearson correlation. + algorithm : str, default: 'pearson' + The ranking algorithm to use, one of: 'pearson', 'covariance', 'spearman', + or 'kendalltau'. features : list A list of feature names to use. - If a DataFrame is passed to fit and features is None, feature - names are selected as the columns of the DataFrame. + If a DataFrame is passed to fit and features is None, feature names are + selected as the columns of the DataFrame. + + colormap : string or cmap, default: 'RdBu_r' + optional string or matplotlib cmap to colorize lines + Use either color to colorize the lines on a per class basis or colormap to + color them on a continuous scale. show_feature_names : boolean, default: True - If True, the feature names are used to label the axis ticks in the - plot. + If True, the feature names are used to label the axis ticks in the plot. - colormap : string or cmap - optional string or matplotlib cmap to colorize lines - Use either color to colorize the lines on a per class basis or - colormap to color them on a continuous scale. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. Returns ------- viz : Rank2D - Returns the fitted, finalized visualizer - + Returns the fitted, finalized visualizer that created the Rank2D heatmap. """ # Instantiate the visualizer - visualizer = Rank2D( + viz = Rank2D( ax=ax, algorithm=algorithm, features=features, @@ -602,8 +611,14 @@ def rank2d( ) # Fit and transform the visualizer (calls draw) - visualizer.fit(X, y, **kwargs) - visualizer.transform(X) + viz.fit(X, y) + viz.transform(X) + + # Show or finalize + if show: + viz.show() + else: + viz.finalize() # Return the visualizer object - return visualizer + return viz From 942360a1699d835c37c2b11c1deebcdc1161e5c4 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Fri, 7 Feb 2020 18:34:24 -0500 Subject: [PATCH 22/44] Make residuals_plot quick method primetime (#1019) Refactors the residuals_plot quick method to the new quick method signature and verifies that it works correctly --- docs/api/regressor/residuals.rst | 33 +++++- .../test_residuals/test_peplot_no_lines.png | Bin 19022 -> 19022 bytes .../test_peplot_no_shared_limits.png | Bin 25321 -> 25321 bytes .../test_residuals/test_prediction_error.png | Bin 24553 -> 24553 bytes .../test_prediction_error_numpy.png | Bin 35273 -> 35273 bytes .../test_prediction_error_pandas.png | Bin 35258 -> 35258 bytes .../test_residuals/test_residuals_plot.png | Bin 59393 -> 59393 bytes .../test_residuals_plot_no_histogram.png | Bin 57836 -> 57836 bytes .../test_residuals_plot_numpy.png | Bin 60848 -> 60848 bytes .../test_residuals_plot_pandas.png | Bin 60848 -> 60848 bytes .../test_residuals_quick_method.png | Bin 51849 -> 57834 bytes ...test_residuals_quick_method_train_only.png | Bin 0 -> 42898 bytes tests/test_regressor/test_residuals.py | 41 +++++++- yellowbrick/regressor/residuals.py | 96 +++++++++++------- 14 files changed, 129 insertions(+), 41 deletions(-) create mode 100644 tests/baseline_images/test_regressor/test_residuals/test_residuals_quick_method_train_only.png diff --git a/docs/api/regressor/residuals.rst b/docs/api/regressor/residuals.rst index 70b60fd15..b06730cfc 100644 --- a/docs/api/regressor/residuals.rst +++ b/docs/api/regressor/residuals.rst @@ -5,6 +5,14 @@ Residuals Plot Residuals, in the context of regression models, are the difference between the observed value of the target variable (y) and the predicted value (ŷ), i.e. the error of the prediction. The residuals plot shows the difference between residuals on the vertical axis and the dependent variable on the horizontal axis, allowing you to detect regions within the target that may be susceptible to more or less error. +================= ============================== +Visualizer :class:`~yellowbrick.regressor.residuals.ResidualsPlot` +Quick Method :func:`~yellowbrick.regressor.residuals.residuals_plot` +Models Regression +Workflow Model evaluation +================= ============================== + + .. plot:: :context: close-figs :alt: Residuals Plot on the Concrete dataset using a linear model @@ -44,10 +52,33 @@ Note that if the histogram is not desired, it can be turned off with the ``hist= .. warning:: The histogram on the residuals plot requires matplotlib 2.0.2 or greater. If you are using an earlier version of matplotlib, simply set the ``hist=False`` flag so that the histogram is not drawn. +Quick Method +------------ + +Similar functionality as above can be achieved in one line using the associated quick method, ``residuals_plot``. This method will instantiate and fit a ``ResidualsPlot`` visualizer on the training data, then will score it on the optionally provided test data (or the training data if it is not provided). + +.. plot:: + :context: close-figs + :alt: residuals_plot on concrete dataset using non-linear model + + from sklearn.ensemble import RandomForestRegressor + from sklearn.model_selection import train_test_split as tts + from yellowbrick.regressor import residuals_plot + from yellowbrick.datasets import load_concrete + + # Load the dataset and split into train/test splits + X, y = load_concrete() + + X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, shuffle=True) + + # Create the visualizer, fit, score, and show it + viz = residuals_plot(RandomForestRegressor(), X_train, y_train, X_test, y_test) + + API Reference ------------- .. automodule:: yellowbrick.regressor.residuals - :members: ResidualsPlot + :members: ResidualsPlot, residuals_plot :undoc-members: :show-inheritance: diff --git 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self.data.X.train, self.data.y.train, ax=ax, random_state=23 + model, + self.data.X.train, + self.data.y.train, + self.data.X.test, + self.data.y.test, + ax=ax, + line_color="#cccccc", + test_color="r", + train_color="y", ) + assert isinstance(oz, ResidualsPlot) self.assert_images_similar(oz) + @pytest.mark.xfail( + IS_WINDOWS_OR_CONDA, + reason="font rendering different in OS and/or Python; see #892", + ) + def test_residuals_quick_method_train_only(self): + """ + Test the quick method with only train data (simplest args) + """ + oz = residuals_plot( + Ridge(random_state=19), + self.data.X.train, + self.data.y.train, + ) + + assert isinstance(oz, ResidualsPlot) + self.assert_images_similar(oz) + + def test_residuals_quick_method_missing_data(self): + """ + Ensure the quick method requires both X_test and y_test if one specified + """ + msg = "both X_test and y_test are required if one is specified" + with pytest.raises(YellowbrickValueError, match=msg): + residuals_plot( + Lasso(), + self.data.X.train, + self.data.y.train, + self.data.X.test, + ) + @pytest.mark.xfail( IS_WINDOWS_OR_CONDA, reason="font rendering different in OS and/or Python; see #892", diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index ac9dd2f5c..a5608cdf8 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -681,28 +681,29 @@ def finalize(self, **kwargs): def residuals_plot( model, - X, - y, + X_train, + y_train, + X_test=None, + y_test=None, ax=None, hist=True, - test_size=0.25, train_color="b", test_color="g", line_color=LINE_COLOR, - random_state=None, train_alpha=0.75, test_alpha=0.75, is_fitted="auto", + show=True, **kwargs ): - """Quick method: + """ResidualsPlot quick method: - Divides the dataset X, y into a train and test split (the size of the - splits determined by test_size) then plots the training and test residuals - agains the predicted value for the given model. + A residual plot shows the residuals on the vertical axis and the + independent variable on the horizontal axis. - This helper function is a quick wrapper to utilize the ResidualsPlot - ScoreVisualizer for one-off analysis. + If the points are randomly dispersed around the horizontal axis, a linear + regression model is appropriate for the data; otherwise, a non-linear + model is more appropriate. Parameters ---------- @@ -712,11 +713,22 @@ def residuals_plot( If the estimator is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by ``is_fitted``. - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features. + X_train : ndarray or DataFrame of shape n x m + A feature array of n instances with m features the model is trained on. + Used to fit the visualizer and also to score the visualizer if test splits are + not directly specified. - y : ndarray or Series of length n - An array or series of target or class values. + y_train : ndarray or Series of length n + An array or series of target or class values. Used to fit the visualizer and + also to score the visualizer if test splits are not specified. + + X_test : ndarray or DataFrame of shape n x m, default: None + An optional feature array of n instances with m features that the model + is scored on if specified, using X_train as the training data. + + y_test : ndarray or Series of length n, default: None + An optional array or series of target or class values that serve as actual + labels for X_test for scoring purposes. ax : matplotlib Axes, default: None The axes to plot the figure on. If None is passed in the current axes @@ -728,11 +740,6 @@ def residuals_plot( If set to 'density', the probability density function will be plotted. If set to True or 'frequency' then the frequency will be plotted. - test_size : float, int default: 0.25 - If float, should be between 0.0 and 1.0 and represent the proportion - of the dataset to include in the test split. If int, represents the - absolute number of test samples. - train_color : color, default: 'b' Residuals for training data are ploted with this color but also given an opacity of 0.5 to ensure that the test data residuals @@ -747,37 +754,39 @@ def residuals_plot( line_color : color, default: dark grey Defines the color of the zero error line, can be any matplotlib color. - random_state : int, RandomState instance or None, optional - Passed to the train_test_split function. - train_alpha : float, default: 0.75 Specify a transparency for traininig data, where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. test_alpha : float, default: 0.75 - Specify a transparency for test data, where 1 is completely opaque and - 0 is completely transparent. This property makes densely clustered + Specify a transparency for test data, where 1 is completely opaque + and 0 is completely transparent. This property makes densely clustered points more visible. - is_fitted : bool or str, default="auto" + is_fitted : bool or str, default='auto' Specify if the wrapped estimator is already fitted. If False, the estimator will be fit when the visualizer is fit, otherwise, the estimator will not be - modified. If "auto" (default), a helper method will check if the estimator + modified. If 'auto' (default), a helper method will check if the estimator is fitted before fitting it again. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. Returns ------- - visualizer : ResidualsPlot - Returns the residuals plot visualizer + viz : ResidualsPlot + Returns the fitted ResidualsPlot that created the figure. """ - # Instantiate the visualizer - visualizer = ResidualsPlot( + # Instantiate the visualizer + viz = ResidualsPlot( model=model, ax=ax, hist=hist, @@ -790,15 +799,24 @@ def residuals_plot( **kwargs ) - # Create the train and test splits - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=test_size, random_state=random_state - ) + # Fit the visualizer + viz.fit(X_train, y_train) - # Fit and transform the visualizer (calls draw) - visualizer.fit(X_train, y_train, **kwargs) - visualizer.score(X_test, y_test) - visualizer.finalize() + # Score the visualizer + if X_test is not None and y_test is not None: + viz.score(X_test, y_test) + elif X_test is not None or y_test is not None: + raise YellowbrickValueError( + "both X_test and y_test are required if one is specified" + ) + else: + viz.score(X_train, y_train) + + # Draw the final visualization + if show: + viz.show() + else: + viz.finalize() # Return the visualizer - return visualizer + return viz From 2671cbab96f2893748cbfe9791acd19589b019bb Mon Sep 17 00:00:00 2001 From: mmorrison1670 <46731745+mmorrison1670@users.noreply.github.com> Date: Fri, 7 Feb 2020 20:44:40 -0500 Subject: [PATCH 23/44] confusion_matrix quickmethod (#1023) Updates the confusion_matrix quick method to the new quick method signature and verifies that it works correctly --- docs/api/classifier/confusion_matrix.rst | 35 +++++++++- .../test_confusion_matrix.png | Bin 17875 -> 17478 bytes .../test_confusion_matrix/test_fontsize.png | Bin 10566 -> 10565 bytes .../test_no_classes_provided.png | Bin 20694 -> 20694 bytes .../test_pandas_integration.png | Bin 6585 -> 6585 bytes .../test_percent_mode.png | Bin 29807 -> 29806 bytes .../test_quick_method.png | Bin 5727 -> 6001 bytes .../test_classifier/test_confusion_matrix.py | 5 +- yellowbrick/classifier/confusion_matrix.py | 61 +++++++++++------- 9 files changed, 75 insertions(+), 26 deletions(-) diff --git a/docs/api/classifier/confusion_matrix.rst b/docs/api/classifier/confusion_matrix.rst index f7a6d8ffc..6b907363b 100644 --- a/docs/api/classifier/confusion_matrix.rst +++ b/docs/api/classifier/confusion_matrix.rst @@ -16,6 +16,14 @@ Below are a few examples of using the ``ConfusionMatrix`` visualizer; more information can be found by looking at the scikit-learn documentation on `confusion matrices `_. +================= ================= +Visualizer `ConfusionMatrix `_ +Quick Method `confusion_matrix() `_ +Models Classification +Workflow Model evaluation +================= ================= + + .. plot:: :context: close-figs :alt: ConfusionMatrix plot of sklearn Digits dataset @@ -83,11 +91,36 @@ Class names can be added to a ``ConfusionMatrix`` plot using the ``label_encoder iris_cm.show() +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method ``confusion_matrix``. This method will build the ``ConfusionMatrix`` object with the associated arguments, fit it, then (optionally) immediately show it. In the below example we can see how a ``LogisticRegression`` struggles to effectively model the credit dataset (hint: check out :class:`~yellowbrick.features.rankd.Rank2D` to examine for multicollinearity!). + +.. plot:: + :context: close-figs + :alt: confusion_matrix on the credit dataset + + from yellowbrick.datasets import load_credit + from yellowbrick.classifier import confusion_matrix + from sklearn.linear_model import LogisticRegression + from sklearn.model_selection import train_test_split as tts + + #Load the classification dataset + X, y = load_credit() + + #Create the train and test data + X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2) + + # Instantiate the visualizer with the classification model + confusion_matrix( + LogisticRegression(), + X_train, y_train, X_test, y_test, + classes=['not_defaulted', 'defaulted'] + ) API Reference ------------- .. automodule:: yellowbrick.classifier.confusion_matrix - :members: ConfusionMatrix + :members: ConfusionMatrix, 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100644 --- a/tests/test_classifier/test_confusion_matrix.py +++ b/tests/test_classifier/test_confusion_matrix.py @@ -348,9 +348,10 @@ def test_quick_method(self): _, ax = plt.subplots() model = DecisionTreeClassifier(random_state=25) - confusion_matrix(model, X, y, ax=ax, random_state=23) - self.assert_images_similar(ax=ax, tol=0.1) + # compare the images + visualizer = confusion_matrix(model, X, y, ax=ax, show=False) + self.assert_images_similar(visualizer) def test_isclassifier(self): """ diff --git a/yellowbrick/classifier/confusion_matrix.py b/yellowbrick/classifier/confusion_matrix.py index 6c06c6a68..488d3ed0e 100644 --- a/yellowbrick/classifier/confusion_matrix.py +++ b/yellowbrick/classifier/confusion_matrix.py @@ -19,7 +19,6 @@ import numpy as np -from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix as confusion_matrix_metric from yellowbrick.utils import div_safe @@ -338,11 +337,11 @@ def finalize(self, **kwargs): def confusion_matrix( model, - X, - y, + X_train, + y_train, + X_test=None, + y_test=None, ax=None, - test_size=0.2, - random_state=None, sample_weight=None, percent=False, classes=None, @@ -351,6 +350,7 @@ def confusion_matrix( fontsize=None, is_fitted="auto", force_model=False, + show=True, **kwargs ): """Confusion Matrix @@ -374,22 +374,28 @@ def confusion_matrix( fitted, it is fit when the visualizer is fitted, unless otherwise specified by ``is_fitted``. - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features. + X_train : array-like, 2D + The table of instance data or independent variables that describe the outcome of + the dependent variable, y. Used to fit the visualizer and also to score the + visualizer if test splits are not specified. - y : ndarray or Series of length n - An array or series of target or class values. + y_train : array-like, 2D + The vector of target data or the dependent variable predicted by X. Used to fit + the visualizer and also to score the visualizer if test splits are not + specified. + + X_test: array-like, 2D, default: None + The table of instance data or independent variables that describe the outcome of + the dependent variable, y. Used to score the visualizer if specified. + + y_test: array-like, 1D, default: None + The vector of target data or the dependent variable predicted by X. Used to + score the visualizer if specified. ax : matplotlib Axes, default: None The axes to plot the figure on. If not specified the current axes will be used (or generated if required). - test_size : float, default=0.2 - The percentage of the data to reserve as test data. - - random_state : int or None, default=None - The value to seed the random number generator for shuffling data. - sample_weight: array-like of shape = [n_samples], optional Passed to ``confusion_matrix`` to weight the samples. @@ -432,6 +438,11 @@ def confusion_matrix( will prevent an exception when the visualizer is initialized but may result in unexpected or unintended behavior. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments passed to the visualizer base classes. @@ -455,16 +466,20 @@ def confusion_matrix( **kwargs ) - # Create the train and test splits - # TODO: determine how to use quick methods that require train and test data. - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=test_size, random_state=random_state - ) - # Fit and transform the visualizer (calls draw) visualizer.fit(X_train, y_train, **kwargs) - visualizer.score(X_test, y_test) - visualizer.finalize() + + # Scores the visualizer with X_test and y_test if provided, + # X_train, y_train if not provided + if X_test is not None and y_test is not None: + visualizer.score(X_test, y_test) + else: + visualizer.score(X_train, y_train) + + if show: + visualizer.show() + else: + visualizer.finalize() # Return the visualizer return visualizer From 216f4580120910496b1474461304e437fb721c59 Mon Sep 17 00:00:00 2001 From: naresh-bachwani <43993586+naresh-bachwani@users.noreply.github.com> Date: Sat, 8 Feb 2020 10:02:51 +0530 Subject: [PATCH 24/44] Postag quick method updates (#992) Refactors the postag quick method to the new quick method signature and verifies that it works correctly. --- docs/api/text/postag.rst | 41 +++++++- tests/test_text/test_postag.py | 2 +- yellowbrick/text/postag.py | 171 ++++++++++++++++++++------------- 3 files changed, 144 insertions(+), 70 deletions(-) diff --git a/docs/api/text/postag.rst b/docs/api/text/postag.rst index 9af40e8dd..4de438ce7 100644 --- a/docs/api/text/postag.rst +++ b/docs/api/text/postag.rst @@ -7,6 +7,13 @@ Parts of speech (e.g. verbs, nouns, prepositions, adjectives) indicate how a wor The ``PosTagVisualizer`` is intended to support grammar-based feature extraction techniques for machine learning workflows that require natural language processing. The visualizer can either read in a corpus that has already been sentence- and word-segmented, and tagged, or perform this tagging automatically by specifying the parser to use (nltk or spacy). The visualizer creates a bar chart to visualize the relative proportions of different parts-of-speech in a corpus. +================= ================= +Visualizer `PosTagVisualizer `_ +Quick Method `postag() `_ +Models Classification, Regression +Workflow Feature Engineering +================= ================= + .. note:: The ``PosTagVisualizer`` currently works with both Penn-Treebank (e.g. via NLTK) and Universal Dependencies (e.g. via SpaCy)-tagged corpora. This expects either raw text, or corpora that have already been tagged which take the form of a list of (document) lists of (sentence) lists of ``(token, tag)`` tuples, as in the example below. @@ -146,6 +153,38 @@ Libraries like SpaCy use tags from the Universal Dependencies (UD) framework. Th viz.fit(tagged_speech) viz.show() +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method ``postag``. This method will build the ``PosTagVisualizer`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. + +.. plot:: + :context: close-figs + :alt: postag quick method with Penn Treebank tags + + from yellowbrick.text.postag import postag + + machado = [ + [ + [ + ('Last', 'JJ'), ('night', 'NN'), ('as', 'IN'), ('I', 'PRP'), + ('was', 'VBD'), ('sleeping', 'VBG'), (',', ','), ('I', 'PRP'), + ('dreamt', 'VBP'), ('—', 'RB'), ('marvelous', 'JJ'), ('error', 'NN'), + ('!—', 'IN'), ('that', 'DT'), ('I', 'PRP'), ('had', 'VBD'), ('a', 'DT'), + ('beehive', 'NN'), ('here', 'RB'), ('inside', 'IN'), ('my', 'PRP$'), + ('heart', 'NN'), ('.', '.') + ], + [ + ('And', 'CC'), ('the', 'DT'), ('golden', 'JJ'), ('bees', 'NNS'), + ('were', 'VBD'), ('making', 'VBG'), ('white', 'JJ'), ('combs', 'NNS'), + ('and', 'CC'), ('sweet', 'JJ'), ('honey', 'NN'), ('from', 'IN'), + ('my', 'PRP$'), ('old', 'JJ'), ('failures', 'NNS'), ('.', '.') + ] + ] + ] + + # Create the visualizer, fit, score, and show it + postag(machado) + +-------------------+------------------------------------------+----------------------+--------------------------+ | Penn-Treebank Tag | Description | Universal Tag | Description | @@ -280,6 +319,6 @@ API Reference ------------- .. automodule:: yellowbrick.text.postag - :members: PosTagVisualizer + :members: PosTagVisualizer, postag :undoc-members: :show-inheritance: \ No newline at end of file diff --git a/tests/test_text/test_postag.py b/tests/test_text/test_postag.py index 23c6ceac3..474803471 100644 --- a/tests/test_text/test_postag.py +++ b/tests/test_text/test_postag.py @@ -173,7 +173,7 @@ def test_quick_method(self): _, ax = plt.subplots() tagged_docs = list(get_tagged_docs(sonnets)) - viz = postag(tagged_docs, ax=ax) + viz = postag(tagged_docs, ax=ax, show=False) viz.ax.grid(False) self.assert_images_similar(viz) diff --git a/yellowbrick/text/postag.py b/yellowbrick/text/postag.py index 7fdb7f7bd..765510222 100644 --- a/yellowbrick/text/postag.py +++ b/yellowbrick/text/postag.py @@ -126,12 +126,13 @@ class PosTagVisualizer(TextVisualizer): Note that fit() requires y for this visualization. parser : string or None, default: None - If set to a string, string must be in the form of 'parser_tagger' or 'parser' to use defaults - (for spacy this is 'en_core_web_sm', for nltk this is 'word'). The 'parser' argument is - one of the accepted parsing libraries. Currently 'nltk' and 'spacy' are the only accepted libraries. - NLTK or SpaCy must be installed into your environment. 'tagger' is the tagset to use. For example - 'nltk_wordpunct' would use the NLTK library with 'wordpunct' tagset. Or 'spacy_en_core_web_sm' would - use SpaCy with the 'en_core_web_sm' tagset. + If set to a string, string must be in the form of 'parser_tagger' or 'parser' + to use defaults (for spacy this is 'en_core_web_sm', for nltk this is 'word'). + The 'parser' argument is one of the accepted parsing libraries. Currently + 'nltk' and 'spacy' are the only accepted libraries. NLTK or SpaCy must be + installed into your environment. 'tagger' is the tagset to use. For example + 'nltk_wordpunct' would use the NLTK library with 'wordpunct' tagset. Or + 'spacy_en_core_web_sm' would use SpaCy with the 'en_core_web_sm' tagset. kwargs : dict Pass any additional keyword arguments to the PosTagVisualizer. @@ -149,15 +150,15 @@ class PosTagVisualizer(TextVisualizer): """ def __init__( - self, - ax=None, - tagset="penn_treebank", - colormap=None, - colors=None, - frequency=False, - stack=False, - parser=None, - **kwargs + self, + ax=None, + tagset="penn_treebank", + colormap=None, + colors=None, + frequency=False, + stack=False, + parser=None, + **kwargs, ): super(PosTagVisualizer, self).__init__(ax=ax, **kwargs) @@ -185,11 +186,11 @@ def parser(self): @parser.setter def parser(self, parser): - accepted_parsers = ('nltk', 'spacy') + accepted_parsers = ("nltk", "spacy") if not parser: self._parser = None elif parser.startswith(accepted_parsers): - parser_tagger = parser.split('_', 1) # split on the first occurrence of _ + parser_tagger = parser.split("_", 1) # split on the first occurrence of _ parser_name = None tagger_name = None @@ -203,36 +204,48 @@ def parser(self, parser): try: importlib.import_module(parser_name) except ModuleNotFoundError: - raise ModuleNotFoundError("Can't find module '{}' in this environment.".format(parser)) + raise ModuleNotFoundError( + "Can't find module '{}' in this environment.".format(parser) + ) - if parser_name == 'nltk': - nltk = importlib.import_module('nltk') + if parser_name == "nltk": + nltk = importlib.import_module("nltk") try: nltk.data.find("corpora/treebank") except LookupError: - raise LookupError("Error occured because nltk postag data is not available") + raise LookupError( + "Error occured because nltk postag data is not available" + ) - nltk_taggers = ['word', 'wordpunct'] + nltk_taggers = ["word", "wordpunct"] if not tagger_name: - tagger_name = 'word' - parser = parser_name + '_' + tagger_name + tagger_name = "word" + parser = parser_name + "_" + tagger_name if tagger_name not in nltk_taggers: - raise ValueError("If using NLTK, tagger should either be 'word' (default) or 'wordpunct'.") + raise ValueError( + "If using NLTK, tagger should either be 'word' (default) or" + " 'wordpunct'." + ) - elif parser_name == 'spacy': + elif parser_name == "spacy": if not tagger_name: - tagger_name = 'en_core_web_sm' - parser = parser_name + '_' + tagger_name + tagger_name = "en_core_web_sm" + parser = parser_name + "_" + tagger_name try: - spacy = importlib.import_module('spacy') + spacy = importlib.import_module("spacy") spacy.load(tagger_name) except OSError: - raise OSError(f"Spacy model '{tagger_name}' has not been downloaded into this environment.") + raise OSError( + "Spacy model '{}' has not been downloaded into this" + " environment.".format(tagger_name) + ) self._parser = parser else: - raise ValueError("{} is an invalid parser. Currently the supported parsers are 'nltk' and " - "'spacy'".format(parser)) + raise ValueError( + "{} is an invalid parser. Currently the supported parsers are 'nltk'" + "and 'spacy'".format(parser) + ) def fit(self, X, y=None, **kwargs): """ @@ -265,8 +278,8 @@ def fit(self, X, y=None, **kwargs): self.labels_ = ["documents"] if self.parser: - parser_name = self.parser.split('_', 1)[0] - parse_func = getattr(self, 'parse_{}'.format(parser_name)) + parser_name = self.parser.split("_", 1)[0] + parse_func = getattr(self, "parse_{}".format(parser_name)) X = parse_func(X) if self.stack: @@ -288,52 +301,59 @@ def fit(self, X, y=None, **kwargs): def parse_nltk(self, X): """ - Tag a corpora using NLTK tagging (Penn-Treebank). + Tag a corpora using NLTK tagging (Penn-Treebank) to produce a generator of + tagged documents in the form of a list of (document) lists of (sentence) + lists of (token, tag) tuples. Parameters ---------- X : str (raw text) or list of paragraphs (containing str) - :return: Generator of tagged documents in the form of a a list of (document) lists of (sentence) - lists of (token, tag) tuples. """ - nltk = importlib.import_module('nltk') + nltk = importlib.import_module("nltk") nltk.data.find("corpora/treebank") - tagger = self.parser.split('_', 1)[1] + tagger = self.parser.split("_", 1)[1] - if tagger == 'word': + if tagger == "word": for doc in X: yield [ - nltk.pos_tag(nltk.word_tokenize(sent)) for sent in nltk.sent_tokenize(doc) + nltk.pos_tag(nltk.word_tokenize(sent)) + for sent in nltk.sent_tokenize(doc) ] - elif tagger == 'wordpunct': + elif tagger == "wordpunct": for doc in X: yield [ - nltk.pos_tag(nltk.wordpunct_tokenize(sent)) for sent in nltk.sent_tokenize(doc) + nltk.pos_tag(nltk.wordpunct_tokenize(sent)) + for sent in nltk.sent_tokenize(doc) ] def parse_spacy(self, X): """ - Tag a corpora using SpaCy tagging (Universal Dependencies). + Tag a corpora using SpaCy tagging (Universal Dependencies) to produce a + generator of tagged documents in the form of a list of (document) + lists of (sentence) lists of (token, tag) tuples. Parameters ---------- X : str (raw text) or list of paragraphs (containing str) - :return: Generator of tagged documents in the form of a a list of (document) lists of (sentence) - lists of (token, tag) tuples. """ - spacy = importlib.import_module('spacy') - tagger = self.parser.split('_', 1)[1] + spacy = importlib.import_module("spacy") + tagger = self.parser.split("_", 1)[1] nlp = spacy.load(tagger) if isinstance(X, list): for doc in X: tagged = nlp(doc) - yield [[(token.text, token.pos_) for token in sents] for sents in tagged.sents] + yield [ + [(token.text, token.pos_) for token in sents] + for sents in tagged.sents + ] elif isinstance(X, str): tagged = nlp(X) - yield [[(token.text, token.pos_) for token in sents] for sents in tagged.sents] + yield [ + [(token.text, token.pos_) for token in sents] for sents in tagged.sents + ] def _penn_tag_map(self): """ @@ -556,16 +576,17 @@ def show(self, outpath=None, **kwargs): def postag( - X, - y=None, - ax=None, - tagset="penn_treebank", - colormap=None, - colors=None, - frequency=False, - stack=False, - parser=None, - **kwargs + X, + y=None, + ax=None, + tagset="penn_treebank", + colormap=None, + colors=None, + frequency=False, + stack=False, + parser=None, + show=True, + **kwargs, ): """ Display a barchart with the counts of different parts of speech @@ -580,6 +601,10 @@ def postag( that yields a list of documents that contain a list of sentences that contain (token, tag) tuples. + y : ndarray or Series of length n + An optional array of target values that are ignored by the + visualizer. + ax : matplotlib axes The axes to plot the figure on. @@ -603,12 +628,18 @@ def postag( Note that fit() requires y for this visualization. parser : string or None, default: None - If set to a string, string must be in the form of 'parser_tagger' or 'parser' to use defaults - (for spacy this is 'en_core_web_sm', for nltk this is 'word'). The 'parser' argument is - one of the accepted parsing libraries. Currently 'nltk' and 'spacy' are the only accepted libraries. - NLTK or SpaCy must be installed into your environment. 'tagger' is the tagset to use. For example - 'nltk_wordpunct' would use the NLTK library with 'wordpunct' tagset. Or 'spacy_en_core_web_sm' would - use SpaCy with the 'en_core_web_sm' tagset. + If set to a string, string must be in the form of 'parser_tagger' or 'parser' + to use defaults (for spacy this is 'en_core_web_sm', for nltk this is 'word'). + The 'parser' argument is one of the accepted parsing libraries. Currently + 'nltk' and 'spacy' are the only accepted libraries. NLTK or SpaCy must be + installed into your environment. 'tagger' is the tagset to use. For example + 'nltk_wordpunct' would use the NLTK library with 'wordpunct' tagset. Or + 'spacy_en_core_web_sm' would use SpaCy with the 'en_core_web_sm' tagset. + + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` kwargs : dict Pass any additional keyword arguments to the PosTagVisualizer. @@ -627,12 +658,16 @@ def postag( frequency=frequency, stack=stack, parser=parser, - **kwargs + **kwargs, ) # Fit and transform the visualizer (calls draw) visualizer.fit(X, y=y, **kwargs) - visualizer.finalize() + + if show: + visualizer.show() + else: + visualizer.finalize() # Return the visualizer object return visualizer From c68df783deee8bf210f7e33f80f9ab2c46d3108c Mon Sep 17 00:00:00 2001 From: Larry Gray Date: Sat, 8 Feb 2020 10:30:20 -0500 Subject: [PATCH 25/44] Make "Class Prediction Error" Quick method primetime (#1026) Refactors the class prediction error quick method to the new quick method signature and verifies that it works correctly. --- .../api/classifier/class_prediction_error.rst | 34 ++++++++- ...est_class_prediction_error_quickmethod.png | Bin 4077 -> 4278 bytes ...on_error_quickmethod_X_test_and_y_test.png | Bin 0 -> 4077 bytes .../test_numpy_integration.png | Bin 4290 -> 4290 bytes .../test_pandas_integration.png | Bin 4290 -> 4290 bytes .../test_class_prediction_error.py | 43 ++++++++++- .../classifier/class_prediction_error.py | 67 +++++++++++------- 7 files changed, 116 insertions(+), 28 deletions(-) create mode 100644 tests/baseline_images/test_classifier/test_class_prediction_error/test_class_prediction_error_quickmethod_X_test_and_y_test.png diff --git a/docs/api/classifier/class_prediction_error.rst b/docs/api/classifier/class_prediction_error.rst index dc20f609a..ec6122b83 100644 --- a/docs/api/classifier/class_prediction_error.rst +++ b/docs/api/classifier/class_prediction_error.rst @@ -7,6 +7,13 @@ The Yellowbrick ``ClassPredictionError`` plot is a twist on other and sometimes The class prediction error chart provides a way to quickly understand how good your classifier is at predicting the right classes. +================= ============================== +Visualizer :class:`~yellowbrick.classifier.class_prediction_error.ClassPredictionError` +Quick Method :func:`~yellowbrick.classifier.class_prediction_error.class_prediction_error` +Models Classification +Workflow Model evaluation +================= ============================== + .. plot:: :context: close-figs :alt: Class Prediction Error plot on Fruit @@ -77,12 +84,37 @@ By contrast, in the following example, the ``RandomForestClassifier`` does a gre # Draw visualization visualizer.show() +Quick Method +------------ + +Similar functionality as above can be achieved in one line using the associated quick method, ``class_prediction_error``. This method will instantiate and fit a ``ClassPredictionError`` visualizer on the training data, then will score it on the optionally provided test data (or the training data if it is not provided). + +.. plot:: + :context: close-figs + :alt: class_prediction_error quick method + + from sklearn.svm import LinearSVC + from sklearn.model_selection import train_test_split as tts + from yellowbrick.classifier import class_prediction_error + from yellowbrick.datasets import load_occupancy + + # Load the dataset and split into train/test splits + X, y = load_occupancy() + X_train, X_test, y_train, y_test = tts( + X, y, test_size=0.2, shuffle=True + ) + + class_prediction_error( + LinearSVC(random_state=42), + X_train, y_train, X_test, y_test, + classes=["vacant", "occupied"] + ) API Reference ------------- .. automodule:: yellowbrick.classifier.class_prediction_error - :members: ClassPredictionError + :members: ClassPredictionError, class_prediction_error :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_classifier/test_class_prediction_error/test_class_prediction_error_quickmethod.png b/tests/baseline_images/test_classifier/test_class_prediction_error/test_class_prediction_error_quickmethod.png index 44ccb4622a36a6a88225b71928e8057223d6b94c..e5c53292cceebe3fc8ac675d577360ad70c1b00e 100644 GIT binary patch literal 4278 zcmeHKc}$aM9DZ90atI#G0CHG@$`nEo3}9D|c_65R&6y20C`SZFghHTHT1#6zI5rU6 z7>GidBg&Q$xnva!Er%0g#lpIASh02-1+ljHRVYH~-lE3nvZ?=U!R$-EqrdNK-{$!} zzvp?r+z*3xn;Ne*1^}1_2JGAqfF2eA^hQGiSow+gpbefdsoMh&7{ZNicrXWkHcAc% zO$A^@lx|1;;9C_Bi(ceiA>@yfV#$Z2Qer@26gfE|iJTB0jitn-q{b(a++DV~xH)6L zAd{2vINbC5E=ehIxD8*O+XKMLcLI05e;~a~e!DK;AwD`wy7^ontH@>(+W5O2HrECk zJx}Yq96zrSNMq5c($BSc1i({<3k7dFO9z%0470j=jIgy5NP=`jW3wyK-WNJcG)wb7i zl*0W$hEVZ#1tJE2o%b>|xNXrV*Jv_Sn66oaC5g^0XMx)giPP(DIl|yG5#zhOT9TlC zA3`W#qkhy8LY32)&SX|VT<|mlpv+9*h#?rkm2rO8Jh64%vF^47olm|U+&`Iyw=olu zIM&agNomSsgKenQXJyn|ikZqd*8vg-*#>x0UTqMA@ylAOq3u6XYFMINh+LR1;ODVh zeWe;?T%UkU;+VGHD8L9ok1RuEd~Li6qwbh{RMKk#?skqkh5+E_&w>MHg@Rl@h%p49 za20k57pd0Xk%^F*B#wC8s@44FyXWUL3*AnW_09rF<8iCP)!4eu;VtDtS+6_GvFs!N z%fy;WN7pV27s9k3z5eIHgbTw{>h{CxhwX4_oP zqKU0Ev{MRSQZ@MqZ)=tv&k^Udciew@SU|8>WnQgVPM`jyOIQ=)u;QHF+r#QN9Lcdn z6{``d(Ul=mWtN?q{Vz&^mll`rvw3d_XR;*Y~QDFig ztz#QXPj(KQNTdRj8q>*Se7S#c>3ejlv@yyys7f?$RNbXv zR9x(zBzxIh7$SMn%<2TAZZz}=CLn<=i|0$A2@#}uZtc)D20yp=FA9g`~OO<(Xkal#dYXM6-0LNsIao^@WB$1}z)XdV^o| z+3r}FU2ybl9In};x?_U8);n`elSCnUL@PJT?zkkD`wQ(UO=14QtTK?>jS%EBb5BiW zKu+;F;xr=rwE9$b59+^8gg3-6p*HtP7N+39svVn|b}g(phGrBRU+anv&wtC`V89XQg?S2UR-GU%ca6Km<&B%@tgjOG!zXg^%0w2&*%n#U+%8 zQ(xDP@g z*L`oNpvg^T7;-Z=q4Wo|VJ!p^_U*~bd7pnBwZ3^{6X(c<(HoxT>>jvnb!qm+iib&t z4`Xme=U~?TjU)Ak*O1#Q9wo&bOkMtIv?MKO#e0(ShZ~wk76y#IjlbPcXNym=C6{)} zop%c$2&%I+62VVJfa~!0#dHQC__}C**1;(>b9#pw5iB%U|D(-iq8dzutU0n^d2v)f z{H>Gnos$yw>eFRi5Txx&%|?oQhxUVky0Me~m+(p}*VL0{8m+j-Q(C8XXh{>SpSI?k zCMw$|Ylt-5yV&uIlQqapC_I%YT&1)=2w;LAAgf*ZLY!Nog6o!fl4lcWwHjMiSjT2C z?I|_}!R~U47iP<4BuIO}vlh<5ye%HkO<~x--t9I$5hT%Y{Ms%0R}~gi$s93(Gpa#$ zEPHP))fg5@A2@tdiCWxs7UMumi9ZT&;VNXL#p2Q4Hi>R*h0BeeH(v-;Q96KHzLl%s zuv(@ow(ke`@UO?;Ik*|L7l2v71d%5BnO12`WO4WBv$LyR{rlj!*h%;;}sst%3QK$L?% zssR6WRDZnH6_^&%|D9ICVlWSVkMed3u#sX}X8Sass&>cPiuh%GW|xWMj~aQ1^4eQU z+F~2}Zdp)<;c%0F0$gVtw1c5&Ahe`)dxZrR5^UF`Y|moEA^4rRU5mKw{iC|0+w5FQ z=#G_ZJL^Tj*?*|~9SBv?rMnFf(LRtd+><6Fk^kf=lW3R-N15$|Ra7FI-%y^e%2aik zrnd^P<$v*%`TCyy4Sk1)QJ%~}?w_bkq+u$b!)}oV%Sx1|ZYUox$oadAa--oK?Byl5 z-V!IvqJ^mdXtT3=DW*qUg^OOp1ZO@fFz7XmH!l!6OOXQ5AVS5_#4=WkF+Smh(kelq zNU?qJD1~?>UDc;pTeNP(G+p&Ft!`Vj(%Mg*dOqdcYYRKj@Wokj%O0or|3W+*I* zXt6y5^Xtsw1>i2xy-oaC$Uh%!$kIF>7Kwh|>0L#EQ(}JytQG&?J-b=u-Ep(qxz*s) zcN3(|P|a7NFulV7u!{t)mDfBubp(X&f9f83v57Q%BGcsI3rCaG_uRv7q@c?*wTu~1 zNEzw1@`;=)_CTTp_CF;3l@)7{}5QI2w3s2=TlD5^W%W5ow z*q1eMGd{U^vbxZ0+@L-u6+PvAY_0~v=}Mh9Yas;YD=Bj`(%nox#ju>;{Slo6;s*Qs z$F5|l9~0d}K{0q-5QmG-9t~(ht*wRYf)wf|d$NvdNDnJ;LdHB63qi#y9DFmz4j+ok bUbBG7t;@)$$AyN#y9VvqRgl-6d+6Lh;*iB; 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A frequency distribution tells us the frequency of each vocabulary item in the text. In general, it could count any kind of observable event. It is a distribution because it tells us how the total number of word tokens in the text are distributed across the vocabulary items. +================= ============================== +Visualizer :class:`~yellowbrick.text.freqdist.FrequencyVisualizer` +Quick Method :func:`~yellowbrick.text.freqdist.freqdist` +Models Text +Workflow Text Analysis +================= ============================== + .. NOTE:: The ``FreqDistVisualizer`` does not perform any normalization or vectorization, and it expects text that has already been count vectorized. We first instantiate a ``FreqDistVisualizer`` object, and then call ``fit()`` on that object with the count vectorized documents and the features (i.e. the words from the corpus), which computes the frequency distribution. The visualizer then plots a bar chart of the top 50 most frequent terms in the corpus, with the terms listed along the x-axis and frequency counts depicted at y-axis values. As with other Yellowbrick visualizers, when the user invokes ``show()``, the finalized visualization is shown. Note that in this plot and in the subsequent one, we can orient our plot vertically by passing in ``orient='v'`` on instantiation (the plot will orient horizontally by default): @@ -120,11 +127,41 @@ And for the gaming corpus (again oriented horizontally): visualizer.fit(docs) visualizer.show() +Quick Method +------------ + +Similar functionality as above can be achieved in one line using the associated quick method, ``freqdist``. This method will instantiate with features(words) and fit a ``FreqDistVisualizer`` visualizer on the documents). + +.. plot:: + :context: close-figs + :alt: Frequency Distribution quick method + + from collections import defaultdict + + from sklearn.feature_extraction.text import CountVectorizer + + from yellowbrick.text import freqdist + from yellowbrick.datasets import load_hobbies + + # Load the text data + corpus = load_hobbies() + + # Create a dict to map target labels to documents of that category + hobbies = defaultdict(list) + for text, label in zip(corpus.data, corpus.target): + hobbies[label].append(text) + + vectorizer = CountVectorizer(stop_words='english') + docs = vectorizer.fit_transform(text for text in hobbies['cinema']) + features = vectorizer.get_feature_names() + + freqdist(features, docs, orient='v') + API Reference ------------- .. automodule:: yellowbrick.text.freqdist - :members: FrequencyVisualizer + :members: FrequencyVisualizer, freqdist :undoc-members: 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test_freqdist_quickmethod(self): + """ + Assert no errors occur during freqdist quickmethod + """ + vectorizer = CountVectorizer() + + docs = vectorizer.fit_transform(corpus.data) + features = vectorizer.get_feature_names() + + viz = freqdist(features, docs, show=False) + + assert isinstance(viz, FreqDistVisualizer) + + # yellowbrick.exceptions.ImageComparisonFailure: images not close (RMS 1.401) + self.assert_images_similar(viz, tol=1.5) diff --git a/yellowbrick/text/freqdist.py b/yellowbrick/text/freqdist.py index 456e77113..865dbf84a 100644 --- a/yellowbrick/text/freqdist.py +++ b/yellowbrick/text/freqdist.py @@ -30,7 +30,9 @@ ########################################################################## -def freqdist(X, y=None, ax=None, n=50, orient="h", color=None, **kwargs): +def freqdist( + features, X, y=None, ax=None, n=50, orient="h", color=None, show=True, **kwargs +): """Displays frequency distribution plot for text. This helper function is a quick wrapper to utilize the FreqDist @@ -39,6 +41,12 @@ def freqdist(X, y=None, ax=None, n=50, orient="h", color=None, **kwargs): Parameters ---------- + features : list, default: None + The list of feature names from the vectorizer, ordered by index. E.g. + a lexicon that specifies the unique vocabulary of the corpus. This + can be typically fetched using the ``get_feature_names()`` method of + the transformer in Scikit-Learn. + X: ndarray or DataFrame of shape n x m A matrix of n instances with m features. In the case of text, X is a list of list of already preprocessed words @@ -58,6 +66,11 @@ def freqdist(X, y=None, ax=None, n=50, orient="h", color=None, **kwargs): color : string Specify color for bars + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however + you cannot call ``plt.savefig`` from this signature, nor + ``clear_figure``. If False, simply calls ``finalize()`` + kwargs: dict Keyword arguments passed to the super class. @@ -67,15 +80,20 @@ def freqdist(X, y=None, ax=None, n=50, orient="h", color=None, **kwargs): Returns the fitted, finalized visualizer """ # Instantiate the visualizer - visualizer = FreqDistVisualizer(ax=ax, n=n, orient=orient, color=color, **kwargs) + viz = FreqDistVisualizer(features, ax=ax, n=n, orient=orient, color=color, **kwargs) # Fit and transform the visualizer (calls draw) - visualizer.fit(X, y, **kwargs) - visualizer.transform(X) - visualizer.finalize() + viz.fit(X, y, **kwargs) + viz.transform(X) + + # Draw the final visualization + if show: + viz.show() + else: + viz.finalize() # Return the visualizer object - return visualizer + return viz class FrequencyVisualizer(TextVisualizer): From eaa501e6f9540997b56a1f99d2a4adb5f6b50988 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sat, 8 Feb 2020 12:37:50 -0500 Subject: [PATCH 27/44] Alphas quick method (#1020) Refactors the alphas quick method to the new quick method signature and verifies that it works correctly --- docs/api/regressor/alphas.rst | 31 +++++++++++++++++- .../test_alphas/test_quick_method.png | Bin 0 -> 18835 bytes .../test_alphas/test_similar_image.png | Bin 18479 -> 18478 bytes tests/test_regressor/test_alphas.py | 17 ++++++++-- yellowbrick/regressor/alphas.py | 14 ++++++-- 5 files changed, 57 insertions(+), 5 deletions(-) create mode 100644 tests/baseline_images/test_regressor/test_alphas/test_quick_method.png diff --git a/docs/api/regressor/alphas.rst b/docs/api/regressor/alphas.rst index 8e17c1163..d8a55eebb 100644 --- a/docs/api/regressor/alphas.rst +++ b/docs/api/regressor/alphas.rst @@ -7,6 +7,14 @@ Regularization is designed to penalize model complexity, therefore the higher th The AlphaSelection Visualizer demonstrates how different values of alpha influence model selection during the regularization of linear models. Generally speaking, alpha increases the affect of regularization, e.g. if alpha is zero there is no regularization and the higher the alpha, the more the regularization parameter influences the final model. +================= ============================== +Visualizer :class:`~yellowbrick.regressor.alphas.AlphaSelection` +Quick Method :func:`~yellowbrick.regressor.alphas.alphas` +Models Regression +Workflow Model selection, Hyperparameter tuning +================= ============================== + + .. plot:: :context: close-figs :alt: Alpha selection on the concrete data set @@ -30,11 +38,32 @@ The AlphaSelection Visualizer demonstrates how different values of alpha influen visualizer.show() +Quick Method +------------ + +The same functionality above can be achieved with the associated quick method `alphas`. This method will build the ``AlphaSelection`` Visualizer object with the associated arguments, fit it, then (optionally) immediately show it. + + +.. plot:: + :context: close-figs + :alt: alphas on the energy dataset + + from sklearn.linear_model import LassoCV + from yellowbrick.regressor.alphas import alphas + + from yellowbrick.datasets import load_energy + + # Load dataset + X, y = load_energy() + + # Use the quick method and immediately show the figure + alphas(LassoCV(random_state=0), X, y) + API Reference ------------- .. automodule:: yellowbrick.regressor.alphas - :members: AlphaSelection, ManualAlphaSelection + :members: AlphaSelection, ManualAlphaSelection, alphas :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_regressor/test_alphas/test_quick_method.png b/tests/baseline_images/test_regressor/test_alphas/test_quick_method.png new file mode 100644 index 0000000000000000000000000000000000000000..3cd292ab30008570e319ed907c64b514fbd1852a GIT binary 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+++++++++++++++++- .../test_umap/test_integrated_umap.png | Bin 43714 -> 41246 bytes .../test_make_classification_umap.png | Bin 28782 -> 29479 bytes ..._make_classification_umap_class_labels.png | Bin 28782 -> 29479 bytes .../test_umap/test_no_target_umap.png | Bin 20300 -> 20169 bytes .../test_text/test_umap/test_quick_method.png | Bin 0 -> 51441 bytes .../test_umap/test_visualizer_with_pandas.png | Bin 28459 -> 30984 bytes tests/test_text/test_umap.py | 16 ++++++++ yellowbrick/text/umap_vis.py | 25 ++++++++++-- 9 files changed, 73 insertions(+), 4 deletions(-) create mode 100644 tests/baseline_images/test_text/test_umap/test_quick_method.png diff --git a/docs/api/text/umap_vis.rst b/docs/api/text/umap_vis.rst index a424634f2..b980e6d4b 100644 --- a/docs/api/text/umap_vis.rst +++ b/docs/api/text/umap_vis.rst @@ -18,6 +18,13 @@ Additionally, it supports a wide variety of distance measures allowing for easy exploration of your data. For a more detailed explanation of the algorithm the paper can be found `here `__. +================= ============================== +Visualizer :class:`~yellowbrick.text.umap_vis.UMAPVisualizer` +Quick Method :func:`~yellowbrick.text.umap_vis.umap` +Models Decomposition +Workflow Feature Engineering/Selection +================= ============================== + In this example, we represent documents via a `term frequency inverse document frequency `__ (TF-IDF) @@ -125,10 +132,37 @@ it disagrees with a non-manifold based clustering technique. A detailed explanation of this phenomenon can be found in this `UMAP documentation `__. + +Quick Method +------------ + +The same functionality above can be achieved with the associated quick method ``umap``. This method will build the ``UMAPVisualizer`` object with the associated arguments, fit it, then (optionally) immediately show it + +.. code:: python + + from sklearn.cluster import KMeans + from sklearn.feature_extraction.text import TfidfVectorizer + + from yellowbrick.datasets import load_hobbies + from yellowbrick.text import UMAPVisualizer + + # Load the text data + corpus = load_hobbies() + + tfidf = TfidfVectorizer() + docs = tfidf.fit_transform(corpus.data) + + # Instantiate the clustering model + clusters = KMeans(n_clusters=5) + clusters.fit(docs) + + umap(docs, ["c{}".format(c) for c in clusters.labels_]) + + API Reference ------------- .. automodule:: yellowbrick.text.umap_vis - :members: UMAPVisualizer + :members: UMAPVisualizer, umap :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_text/test_umap/test_integrated_umap.png b/tests/baseline_images/test_text/test_umap/test_integrated_umap.png index 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zkuPJMtGO~pQB(idknewtw6L$+-Se?Wul^%Xs?q3}g+O2~SNbSwdJGr7Suof4f(a7L zn6M!56%mh*-2E$!Y<4cVjG{#{!+>Y1-!|BAS>zvO3PFLGEwF3_CPxcEX2MP@`!#md zKm**7Aadr2@u>a$uqBt|wt*gaT%axM}<;FyR#ps<<+(*M3e2HBhmo9wun^8%d z{Qbn0A{$J2X}}Zk2kU(SMquIbDz3fCON@Qsa!(k-ae}1f7I|DC=}WpyBZ$Ml2=`UDeiS5Us~l1e6y%Uc;N2O^^a0%73i|7(^10|Ni|(f=no`0uxA Date: Sat, 8 Feb 2020 14:53:18 -0500 Subject: [PATCH 29/44] Make ICDM quick method prime time (#1028) Minor update to the intercluster_distance quick method to the new signature and verification that it works correctly. --- docs/api/cluster/icdm.rst | 29 ++++++++++++++++++++++++++++- tests/test_cluster/test_icdm.py | 4 +++- yellowbrick/cluster/icdm.py | 13 ++++++++++++- 3 files changed, 43 insertions(+), 3 deletions(-) diff --git a/docs/api/cluster/icdm.rst b/docs/api/cluster/icdm.rst index 70b5ecfc4..274200659 100644 --- a/docs/api/cluster/icdm.rst +++ b/docs/api/cluster/icdm.rst @@ -5,6 +5,14 @@ Intercluster Distance Maps Intercluster distance maps display an embedding of the cluster centers in 2 dimensions with the distance to other centers preserved. E.g. the closer to centers are in the visualization, the closer they are in the original feature space. The clusters are sized according to a scoring metric. By default, they are sized by membership, e.g. the number of instances that belong to each center. This gives a sense of the relative importance of clusters. Note however, that because two clusters overlap in the 2D space, it does not imply that they overlap in the original feature space. +================= ============================== +Visualizer :class:`~yellowbrick.cluster.icdm.InterclusterDistance` +Quick Method :func:`~yellowbrick.cluster.icdm.intercluster_distance` +Models Clustering +Workflow Model evaluation +================= ============================== + + .. plot:: :context: close-figs :alt: Intercluster Distance Visualizer on dataset with 12 random clusters @@ -24,10 +32,29 @@ Intercluster distance maps display an embedding of the cluster centers in 2 dime visualizer.fit(X) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure + +Quick Method +------------ + +The same functionality above can be achieved with the associated quick method `intercluster_distance`. This method will build the ``InterclusterDistance`` object with the associated arguments, fit it, then (optionally) immediately show it. + +.. plot:: + :context: close-figs + :alt: ICDM Quick Method + + from yellowbrick.datasets import load_nfl + from sklearn.cluster import MiniBatchKMeans + from yellowbrick.cluster import intercluster_distance + + + X, _ = load_nfl() + intercluster_distance(MiniBatchKMeans(5, random_state=777), X) + + API Reference ------------- .. automodule:: yellowbrick.cluster.icdm - :members: InterclusterDistance + :members: InterclusterDistance, intercluster_distance :undoc-members: :show-inheritance: diff --git a/tests/test_cluster/test_icdm.py b/tests/test_cluster/test_icdm.py index c6ac439da..21f92f060 100644 --- a/tests/test_cluster/test_icdm.py +++ b/tests/test_cluster/test_icdm.py @@ -256,7 +256,9 @@ def test_quick_method(self): Test the quick method producing a valid visualization """ model = MiniBatchKMeans(3, random_state=343) - oz = intercluster_distance(model, self.blobs4.X, random_state=93, legend=False) + oz = intercluster_distance( + model, self.blobs4.X, random_state=93, legend=False, show=False + ) assert isinstance(oz, InterclusterDistance) self.assert_images_similar(oz) diff --git a/yellowbrick/cluster/icdm.py b/yellowbrick/cluster/icdm.py index 7b007845f..6a62d9074 100644 --- a/yellowbrick/cluster/icdm.py +++ b/yellowbrick/cluster/icdm.py @@ -480,6 +480,7 @@ def intercluster_distance( legend_size=1.5, random_state=None, is_fitted="auto", + show=True, **kwargs ): """Quick Method: @@ -559,6 +560,11 @@ def intercluster_distance( modified. If 'auto' (default), a helper method will check if the estimator is fitted before fitting it again. + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however + you cannot call ``plt.savefig`` from this signature, nor + ``clear_figure``. If False, simply calls ``finalize()`` + kwargs : dict Keyword arguments passed to the base class and may influence the feature visualization properties. @@ -584,5 +590,10 @@ def intercluster_distance( ) oz.fit(X, y) - oz.finalize() + + if show: + oz.show() + else: + oz.finalize() + return oz From a0e281ec3bdf753aa025b38547b829f145797373 Mon Sep 17 00:00:00 2001 From: pdamodaran Date: Sat, 8 Feb 2020 16:03:36 -0500 Subject: [PATCH 30/44] Added quick method for Prediction Error (#1022) The PR updates the prediction error quick method with the new show parameter, removes unused parameters in the base class and quick method, adds test coverage for the quick method, and updates the documentation. --- docs/api/model_selection/learning_curve.rst | 30 +++++- docs/api/regressor/peplot.rst | 32 ++++++- .../test_prediction_error_quick_method.png | Bin 0 -> 23936 bytes .../test_learning_curve.py | 1 + tests/test_regressor/test_residuals.py | 17 ++++ .../model_selection/cross_validation.py | 2 +- yellowbrick/model_selection/learning_curve.py | 14 ++- yellowbrick/regressor/residuals.py | 86 ++++++++++++------ 8 files changed, 149 insertions(+), 33 deletions(-) create mode 100644 tests/baseline_images/test_regressor/test_residuals/test_prediction_error_quick_method.png diff --git a/docs/api/model_selection/learning_curve.rst b/docs/api/model_selection/learning_curve.rst index 940e6ed17..700b9b5e9 100644 --- a/docs/api/model_selection/learning_curve.rst +++ b/docs/api/model_selection/learning_curve.rst @@ -3,6 +3,13 @@ Learning Curve ============== +================= ===================== + Visualizer :class:`~yellowbrick.model_selection.learning_curve.LearningCurve` + Quick Method :func:`~yellowbrick.model_selection.learning_curve.learning_curve` + Models Classification, Regression, Clustering + Workflow Model Selection +================= ===================== + A learning curve shows the relationship of the training score versus the cross validated test score for an estimator with a varying number of training samples. This visualization is typically used to show two things: 1. How much the estimator benefits from more data (e.g. do we have "enough data" or will the estimator get better if used in an online fashion). @@ -110,14 +117,33 @@ Learning curves also work for clustering models and can use metrics that specify Unfortunately, with random data these curves are highly variable, but serve to point out some clustering-specific items. First, note the y-axis is very narrow, roughly speaking these curves are converged and actually the clustering algorithm is performing very well. Second, for clustering, convergence for data points is not necessarily a bad thing; in fact we want to ensure as more data is added, the training and cross-validation scores do not diverge. +Quick Method +------------ +The same functionality can be achieved with the associated quick method ``learning_curve``. This method will build the ``LearningCurve`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. + +.. plot:: + :context: close-figs + :alt: Learning Curve Quick Method on the Energy dataset using RidgeCV + + from sklearn.linear_model import RidgeCV + + from yellowbrick.datasets import load_energy + from yellowbrick.model_selection import learning_curve + + # Load a regression dataset + X, y = load_energy() + + # Instantiate the regression model and visualizer + model = RidgeCV() + visualizer = learning_curve(model, X, y, scoring='r2') + .. seealso:: This visualizer is based on the validation curve described in the scikit-learn documentation: `Learning Curves `_. The visualizer wraps the `learning_curve `_ function and most of the arguments are passed directly to it. - API Reference ------------- .. automodule:: yellowbrick.model_selection.learning_curve - :members: LearningCurve + :members: LearningCurve, learning_curve :undoc-members: :show-inheritance: diff --git a/docs/api/regressor/peplot.rst b/docs/api/regressor/peplot.rst index a6758993f..af29b518b 100644 --- a/docs/api/regressor/peplot.rst +++ b/docs/api/regressor/peplot.rst @@ -5,6 +5,13 @@ Prediction Error Plot A prediction error plot shows the actual targets from the dataset against the predicted values generated by our model. This allows us to see how much variance is in the model. Data scientists can diagnose regression models using this plot by comparing against the 45 degree line, where the prediction exactly matches the model. +================= ============================== + Visualizer :class:`~yellowbrick.regressor.residuals.PredictionError` + Quick Method :func:`~yellowbrick.regressor.residuals.prediction_error` + Models Regression + Workflow Model Evaluation +================= ============================== + .. plot:: :context: close-figs :alt: Prediction Error plot on the Concrete dataset using a linear model @@ -28,11 +35,34 @@ A prediction error plot shows the actual targets from the dataset against the pr visualizer.score(X_test, y_test) # Evaluate the model on the test data visualizer.show() # Finalize and render the figure +Quick Method +------------ +The same functionality can be achieved with the associated quick method ``prediction_error``. This method will build the ``PredictionError`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. + +.. plot:: + :context: close-figs + :alt: Prediction Error plot on the Concrete dataset using a linear model + + from sklearn.model_selection import train_test_split + from sklearn.linear_model import Lasso + from yellowbrick.datasets import load_concrete + from yellowbrick.regressor import prediction_error + + # Load a regression dataset + X, y = load_concrete() + + # Create the train and test data + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) + + # Instantiate the linear model and visualizer + model = Lasso() + visualizer = prediction_error(model, X_train, y_train, X_test, y_test) + API Reference ------------- .. automodule:: yellowbrick.regressor.residuals - :members: PredictionError + :members: PredictionError, prediction_error :undoc-members: :show-inheritance: :noindex: \ No newline at 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7f9e5fb36..254bafc05 100644 --- a/tests/test_model_selection/test_learning_curve.py +++ b/tests/test_model_selection/test_learning_curve.py @@ -133,6 +133,7 @@ def test_quick_method(self): cv=ShuffleSplit(n_splits=10, test_size=0.2, random_state=34), scoring="f1_macro", random_state=43, + show=False ) self.assert_images_similar(viz) diff --git a/tests/test_regressor/test_residuals.py b/tests/test_regressor/test_residuals.py index ddffee9ac..5d11846fc 100644 --- a/tests/test_regressor/test_residuals.py +++ b/tests/test_regressor/test_residuals.py @@ -495,3 +495,20 @@ def test_residuals_with_fitted(self): oz = ResidualsPlot(model, is_fitted=False) oz.fit(X, y) mockfit.assert_called_once_with(X, y) + + @pytest.mark.xfail( + IS_WINDOWS_OR_CONDA, + reason="font rendering different in OS and/or Python; see #892", + ) + def test_prediction_error_quick_method(self): + """ + Image similarity test using the residuals plot quick method + """ + _, ax = plt.subplots() + + model = Lasso(random_state=19) + oz = prediction_error( + model, self.data.X.train, self.data.y.train, ax=ax, show=False + ) + assert isinstance(oz, PredictionError) + self.assert_images_similar(oz) diff --git a/yellowbrick/model_selection/cross_validation.py b/yellowbrick/model_selection/cross_validation.py index 287f815b1..68af2fadd 100644 --- a/yellowbrick/model_selection/cross_validation.py +++ b/yellowbrick/model_selection/cross_validation.py @@ -262,7 +262,7 @@ def cv_scores( """ # Initialize the visualizer - visualizer = CVScores(model, ax=ax, cv=cv, scoring=scoring, color=None) + visualizer = CVScores(model, ax=ax, cv=cv, scoring=scoring, color=None, **kwargs) # Fit and show the visualizer visualizer.fit(X, y) diff --git a/yellowbrick/model_selection/learning_curve.py b/yellowbrick/model_selection/learning_curve.py index 1f432dfac..eaddc5540 100644 --- a/yellowbrick/model_selection/learning_curve.py +++ b/yellowbrick/model_selection/learning_curve.py @@ -308,7 +308,7 @@ def finalize(self, **kwargs): ########################################################################## -# Quick Methods +# Quick Method ########################################################################## @@ -326,6 +326,7 @@ def learning_curve( pre_dispatch="all", shuffle=False, random_state=None, + show=True, **kwargs ): """ @@ -408,6 +409,11 @@ def learning_curve( If None, the random number generator is the RandomState instance used by `np.random`. Used when ``shuffle`` is True. + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however + you cannot call ``plt.savefig`` from this signature, nor + ``clear_figure``. If False, simply calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. These arguments are @@ -432,9 +438,13 @@ def learning_curve( shuffle=shuffle, random_state=random_state, exploit_incremental_learning=exploit_incremental_learning, + **kwargs ) # Fit and show the visualizer oz.fit(X, y) - oz.show(**kwargs) + if show: + oz.show() + else: + oz.finalize() return oz diff --git a/yellowbrick/regressor/residuals.py b/yellowbrick/regressor/residuals.py index a5608cdf8..5d5e4b123 100644 --- a/yellowbrick/regressor/residuals.py +++ b/yellowbrick/regressor/residuals.py @@ -27,8 +27,6 @@ except ImportError: make_axes_locatable = None -from sklearn.model_selection import train_test_split - from yellowbrick.draw import manual_legend from yellowbrick.utils.decorators import memoized from yellowbrick.style.palettes import LINE_COLOR @@ -84,12 +82,6 @@ class PredictionError(RegressionScoreVisualizer): model is over- or under- estimating the given values. The color of the identity line is a muted version of the ``line_color`` argument. - point_color : color - Defines the color of the error points; can be any matplotlib color. - - line_color : color - Defines the color of the best fit line; can be any matplotlib color. - alpha : float, default: 0.75 Specify a transparency where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. @@ -157,7 +149,7 @@ def __init__( self.identity = identity self.alpha = alpha - def score(self, X, y=None, **kwargs): + def score(self, X, y, **kwargs): """ The score function is the hook for visual interaction. Pass in test data and the visualizer will create predictions on the data and @@ -285,7 +277,20 @@ def finalize(self, **kwargs): self.ax.legend(loc="best", frameon=True) -def prediction_error(model, X, y=None, ax=None, alpha=0.75, is_fitted="auto", **kwargs): +def prediction_error( + model, + X_train, + y_train, + X_test=None, + y_test=None, + ax=None, + shared_limits=True, + bestfit=True, + identity=True, + alpha=0.75, + is_fitted="auto", + show=True, + **kwargs): """Quickly plot a prediction error visualizer Plot the actual targets from the dataset against the @@ -302,11 +307,22 @@ def prediction_error(model, X, y=None, ax=None, alpha=0.75, is_fitted="auto", ** If the estimator is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by ``is_fitted``. - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features. + X_train : ndarray or DataFrame of shape n x m + A feature array of n instances with m features the model is trained on. + Used to fit the visualizer and also to score the visualizer if test splits are + not directly specified. + + y_train : ndarray or Series of length n + An array or series of target or class values. Used to fit the visualizer and + also to score the visualizer if test splits are not specified. - y : ndarray or Series of length n - An array or series of target or class values. + X_test : ndarray or DataFrame of shape n x m, default: None + An optional feature array of n instances with m features that the model + is scored on if specified, using X_train as the training data. + + y_test : ndarray or Series of length n, default: None + An optional array or series of target or class values that serve as actual + labels for X_test for scoring purposes. ax : matplotlib Axes The axes to plot the figure on. @@ -330,12 +346,6 @@ def prediction_error(model, X, y=None, ax=None, alpha=0.75, is_fitted="auto", ** model is over- or under- estimating the given values. The color of the identity line is a muted version of the ``line_color`` argument. - point_color : color - Defines the color of the error points; can be any matplotlib color. - - line_color : color - Defines the color of the best fit line; can be any matplotlib color. - alpha : float, default: 0.75 Specify a transparency where 1 is completely opaque and 0 is completely transparent. This property makes densely clustered points more visible. @@ -346,6 +356,11 @@ def prediction_error(model, X, y=None, ax=None, alpha=0.75, is_fitted="auto", ** modified. If 'auto' (default), a helper method will check if the estimator is fitted before fitting it again. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -356,15 +371,32 @@ def prediction_error(model, X, y=None, ax=None, alpha=0.75, is_fitted="auto", ** Returns the axes that the prediction error plot was drawn on. """ # Instantiate the visualizer - visualizer = PredictionError(model, ax, alpha=alpha, is_fitted=is_fitted, **kwargs) + visualizer = PredictionError( + model, + ax, + shared_limits=shared_limits, + bestfit=bestfit, + identity=identity, + alpha=alpha, + is_fitted=is_fitted, + **kwargs) + + visualizer.fit(X_train, y_train) - # Create the train and test splits - X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) + # Scores the visualizer with X and y test if provided, X and y train if not + if X_test is not None and y_test is not None: + visualizer.score(X_test, y_test) + elif X_test is not None or y_test is not None: + raise YellowbrickValueError( + "both X_test and y_test are required if one is specified" + ) + else: + visualizer.score(X_train, y_train) - # Fit and transform the visualizer (calls draw) - visualizer.fit(X_train, y_train, **kwargs) - visualizer.score(X_test, y_test) - visualizer.finalize() + if show: + visualizer.show() + else: + visualizer.finalize() # Return the axes object on the visualizer return visualizer From 19cac73e4a0fda40d49572fd73d9c0317409b5be Mon Sep 17 00:00:00 2001 From: pdamodaran Date: Sat, 8 Feb 2020 18:45:24 -0500 Subject: [PATCH 31/44] Updated quick method for Learning Curve (#1013) This PR updates the learning curve quick method to the new function signature and ensures it functions as expected --- docs/api/model_selection/learning_curve.rst | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/api/model_selection/learning_curve.rst b/docs/api/model_selection/learning_curve.rst index 700b9b5e9..ed90527f9 100644 --- a/docs/api/model_selection/learning_curve.rst +++ b/docs/api/model_selection/learning_curve.rst @@ -133,9 +133,7 @@ The same functionality can be achieved with the associated quick method ``learni # Load a regression dataset X, y = load_energy() - # Instantiate the regression model and visualizer - model = RidgeCV() - visualizer = learning_curve(model, X, y, scoring='r2') + learning_curve(RidgeCV(), X, y, scoring='r2') .. seealso:: This visualizer is based on the validation curve described in the scikit-learn documentation: `Learning Curves `_. The visualizer wraps the `learning_curve `_ function and most of the arguments are passed directly to it. From e393b891448067bae1d72efdded0c19e0e65e356 Mon Sep 17 00:00:00 2001 From: Michael Garod Date: Sat, 8 Feb 2020 19:56:03 -0500 Subject: [PATCH 32/44] KElbow quick method API update (#987) Updates kelbow quick method with new function signature and ensure it works as expected --- docs/api/cluster/elbow.rst | 27 +++++++++++++++++++- tests/test_cluster/test_elbow.py | 2 +- yellowbrick/cluster/elbow.py | 44 ++++++++++++++++++++++---------- 3 files changed, 58 insertions(+), 15 deletions(-) diff --git a/docs/api/cluster/elbow.rst b/docs/api/cluster/elbow.rst index 4c8072202..eef5badd1 100644 --- a/docs/api/cluster/elbow.rst +++ b/docs/api/cluster/elbow.rst @@ -7,6 +7,13 @@ The ``KElbowVisualizer`` implements the "elbow" method to help data scientists s To demonstrate, in the following example the ``KElbowVisualizer`` fits the ``KMeans`` model for a range of :math:`K` values from 4 to 11 on a sample two-dimensional dataset with 8 random clusters of points. When the model is fit with 8 clusters, we can see a line annotating the "elbow" in the graph, which in this case we know to be the optimal number. +================= ============================== +Visualizer `KElbowVisualizer `_ +Quick Method `kelbow_visualizer() `_ +Models Clustering +Workflow Model evaluation +================= ============================== + .. plot:: :context: close-figs :alt: KElbowVisualizer on synthetic dataset with 8 random clusters @@ -81,10 +88,28 @@ In the following example, we'll use the ``calinski_harabasz`` score and turn off It is important to remember that the "elbow" method does not work well if the data is not very clustered. In this case, you might see a smooth curve and the optimal value of :math:`K` will be unclear. +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method ``kelbow_visualizer``. This method will build the ``KElbowVisualizer`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. + +.. plot:: + :context: close-figs + :alt: KElbowVisualizer + + from sklearn.cluster import KMeans + from yellowbrick.cluster.elbow import kelbow_visualizer + from yellowbrick.datasets.loaders import load_nfl + + X, y = load_nfl() + + # Use the quick method and immediately show the figure + kelbow_visualizer(KMeans(random_state=4), X, k=(2,10)) + + API Reference ------------- .. automodule:: yellowbrick.cluster.elbow - :members: KElbowVisualizer + :members: KElbowVisualizer, kelbow_visualizer :undoc-members: :show-inheritance: diff --git a/tests/test_cluster/test_elbow.py b/tests/test_cluster/test_elbow.py index 0159960f3..894169612 100644 --- a/tests/test_cluster/test_elbow.py +++ b/tests/test_cluster/test_elbow.py @@ -410,7 +410,7 @@ def test_quick_method(self): ) model = MiniBatchKMeans(3, random_state=43) - oz = kelbow_visualizer(model, X, random_state=13, legend=False) + oz = kelbow_visualizer(model, X, show=False) assert isinstance(oz, KElbowVisualizer) self.assert_images_similar(oz) diff --git a/yellowbrick/cluster/elbow.py b/yellowbrick/cluster/elbow.py index 603c16674..9c720e49d 100644 --- a/yellowbrick/cluster/elbow.py +++ b/yellowbrick/cluster/elbow.py @@ -415,24 +415,31 @@ def finalize(self): ## Quick Method ########################################################################## - def kelbow_visualizer( model, X, y=None, - k=10, ax=None, + k=10, + metric="distortion", timings=True, locate_elbow=True, - metric="distortion", + show=True, **kwargs ): """ Quick Method: model : a Scikit-Learn clusterer - Should be an instance of an unfitted clusterer, specifically ``KMeans`` or - ``MiniBatchKMeans``. If it is not a clusterer, an exception is raised. + Should be an instance of an unfitted clusterer, specifically + ``KMeans`` or ``MiniBatchKMeans``. If it is not a clusterer, an + exception is raised. + + X : array-like of shape (n, m) + A matrix or data frame with n instances and m features + + y : array-like of shape (n,), optional + A vector or series representing the target for each instance ax : matplotlib Axes, default: None The axes to plot the figure on. If None is passed in the current axes @@ -450,7 +457,8 @@ def kelbow_visualizer( observation and its closest centroid. Other metrics include: - **distortion**: mean sum of squared distances to centers - - **silhouette**: mean ratio of intra-cluster and nearest-cluster distance + - **silhouette**: mean ratio of intra-cluster and nearest-cluster + distance - **calinski_harabasz**: ratio of within to between cluster dispersion timings : bool, default: True @@ -458,11 +466,17 @@ def kelbow_visualizer( to train the clustering model. locate_elbow : bool, default: True - Automatically find the "elbow" or "knee" which likely corresponds to the optimal - value of k using the "knee point detection algorithm". The knee point detection - algorithm finds the point of maximum curvature, which in a well-behaved - clustering problem also represents the pivot of the elbow curve. The point is - labeled with a dashed line and annotated with the score and k values. + Automatically find the "elbow" or "knee" which likely corresponds to + the optimal value of k using the "knee point detection algorithm". The + knee point detection algorithm finds the point of maximum curvature, + which in a well-behaved clustering problem also represents the pivot + of the elbow curve. The point is labeled with a dashed line and + annotated with the score and k values. + + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however + you cannot call ``plt.savefig`` from this signature, nor + ``clear_figure``. If False, simply calls ``finalize()`` kwargs : dict Keyword arguments that are passed to the base class and may influence @@ -482,7 +496,11 @@ def kelbow_visualizer( locate_elbow=locate_elbow, **kwargs ) - oz.fit(X, y) - oz.finalize() + + if show: + oz.show() + else: + oz.finalize() + return oz From e1ea0af877c132970c4b0940ced419f8b16998a5 Mon Sep 17 00:00:00 2001 From: mchestnut91 <40185521+mchestnut91@users.noreply.github.com> Date: Sat, 8 Feb 2020 21:11:33 -0500 Subject: [PATCH 33/44] added feature_correlations quick method (#1021) This PR updates the feature_correlations quick method to match the new function signature and ensures it works as expected. --- docs/api/target/feature_correlation.rst | 31 ++++++++++++++++-- .../test_feature_correlation_quick_method.png | Bin 3769 -> 3769 bytes tests/test_target/test_feature_correlation.py | 6 ++-- yellowbrick/target/feature_correlation.py | 18 +++++++--- 4 files changed, 44 insertions(+), 11 deletions(-) diff --git a/docs/api/target/feature_correlation.rst b/docs/api/target/feature_correlation.rst index 290e99e97..8f1c944e3 100644 --- a/docs/api/target/feature_correlation.rst +++ b/docs/api/target/feature_correlation.rst @@ -9,7 +9,14 @@ This visualization can be used in feature selection to identify features with hi Pearson Correlation ------------------- -The default calculation is Pearson correlation, which is perform with ``scipy.stats.pearsonr``. +The default calculation is Pearson correlation, which is performed with ``scipy.stats.pearsonr``. + +================= ================= +Visualizer `FeatureCorrelation `_ +Quick Method `feature_correlation() `_ +Models Regression/Classification/Clustering +Workflow Feature Engineering/Model Selection +================= ================= .. plot:: :context: close-figs @@ -94,11 +101,31 @@ This visualizer also allows sorting of the bar plot according to the calculated visualizer.fit(X_pd, y) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method ``feature_correlation``. This method will build the ``FeatureCorrelation`` object with the associated arguments, fit it, then (optionally) immediately show it + +.. plot:: + :context: close-figs + :alt: feature_correlation on the diabetes dataset + + import numpy as np + from sklearn import datasets + import matplotlib.pyplot as plt + from yellowbrick.target.feature_correlation import feature_correlation + + #Load the diabetes dataset + data = datasets.load_iris() + X, y = data['data'], data['target'] + + features = np.array(data['feature_names']) + visualizer = feature_correlation(X, y, labels=features) + plt.tight_layout() API Reference ------------- .. automodule:: yellowbrick.target.feature_correlation - :members: FeatureCorrelation + :members: FeatureCorrelation, feature_correlation :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_target/test_feature_correlation/test_feature_correlation_quick_method.png b/tests/baseline_images/test_target/test_feature_correlation/test_feature_correlation_quick_method.png index 46de93cdf46131e49fead02bbde115e5352bbd47..53fa3ffb27cedffa8340851033466d7e0210f3c9 100644 GIT binary patch delta 20 bcmdlfyHj>TE~D|pJPDQt21A#PUHW_gNr(n! delta 20 ccmdlfyHj>TE~CN3JPDSqZ`;K;cIop008*z1t^fc4 diff --git a/tests/test_target/test_feature_correlation.py b/tests/test_target/test_feature_correlation.py index 5c03cad5f..22556f41a 100644 --- a/tests/test_target/test_feature_correlation.py +++ b/tests/test_target/test_feature_correlation.py @@ -21,7 +21,7 @@ import pytest import numpy as np import numpy.testing as npt -import matplotlib.pyplot as plt + from yellowbrick.target import FeatureCorrelation, feature_correlation from yellowbrick.exceptions import YellowbrickValueError, YellowbrickWarning @@ -198,8 +198,6 @@ def test_feature_correlation_quick_method(self): """ Test sorting of correlation """ - fig = plt.figure() - ax = fig.add_subplot() - g = feature_correlation.feature_correlation(self.X, self.y, ax) + g = feature_correlation.feature_correlation(self.X, self.y, labels=self.labels, show=False) self.assert_images_similar(g) diff --git a/yellowbrick/target/feature_correlation.py b/yellowbrick/target/feature_correlation.py index 04596a025..2a54e1fb4 100644 --- a/yellowbrick/target/feature_correlation.py +++ b/yellowbrick/target/feature_correlation.py @@ -276,6 +276,7 @@ def feature_correlation( feature_index=None, feature_names=None, color=None, + show=True, **kwargs ): """ @@ -307,7 +308,6 @@ def feature_correlation( from ``sklearn.feature_selection`` - 'mutual_info-classification', which uses ``mutual_info_classif`` from ``sklearn.feature_selection`` - 'mutual_info-classification'], default: 'pearson' labels : list, default: None A list of feature names to use. If a DataFrame is passed to fit and @@ -328,6 +328,11 @@ def feature_correlation( color: string Specify color for barchart + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -339,7 +344,7 @@ def feature_correlation( """ # Instantiate the visualizer - viz = FeatureCorrelation( + visualizer = FeatureCorrelation( ax=ax, method=method, labels=labels, @@ -351,8 +356,11 @@ def feature_correlation( ) # Fit and transform the visualizer (calls draw) - viz.fit(X, y, **kwargs) - viz.finalize() + visualizer.fit(X, y, **kwargs) + if show: + visualizer.show() + else: + visualizer.finalize() # Return the visualizer - return viz + return visualizer From 9833ae6d61d3993691006b4469375ef0bae36dbe Mon Sep 17 00:00:00 2001 From: Larry Gray Date: Sat, 8 Feb 2020 23:06:31 -0500 Subject: [PATCH 34/44] Updates Cooks distance quick method for Prime time (#1027) Updates cooks distance quick method with new function signature and verifies that it works as expected. --- docs/api/regressor/influence.rst | 31 +++++++++++++++++++++++++- tests/test_regressor/test_influence.py | 1 + yellowbrick/regressor/influence.py | 16 +++++++++++-- 3 files changed, 45 insertions(+), 3 deletions(-) diff --git a/docs/api/regressor/influence.rst b/docs/api/regressor/influence.rst index 8ee229a2d..3c0221610 100644 --- a/docs/api/regressor/influence.rst +++ b/docs/api/regressor/influence.rst @@ -10,6 +10,13 @@ models. The ``CooksDistance`` visualizer shows a stem plot of all instances by i and their associated distance score, along with a heuristic threshold to quickly show what percent of the dataset may be impacting OLS regression models. +================= ============================== +Visualizer :class:`~yellowbrick.regressor.influence.CooksDistance` +Quick Method :func:`~yellowbrick.regressor.influence.cooks_distance` +Models General Linear Models +Workflow Dataset/Sensitivity Analysis +================= ============================== + .. plot:: :context: close-figs :alt: Cook's distance using concrete dataset @@ -25,12 +32,34 @@ what percent of the dataset may be impacting OLS regression models. visualizer.fit(X, y) visualizer.show() +Quick Method +------------ + +Similar functionality as above can be achieved in one line using the associated quick method, ``class_prediction_error``. This method will instantiate and fit a ``ClassPredictionError`` visualizer on the training data, then will score it on the optionally provided test data (or the training data if it is not provided). + +.. plot:: + :context: close-figs + :alt: cooks_distance quick method + + from yellowbrick.datasets import load_concrete + from yellowbrick.regressor import cooks_distance + + # Load the regression dataset + X, y = load_concrete() + + # Instantiate and fit the visualizer + cooks_distance( + X, y, + draw_threshold=True, + linefmt="C0-", markerfmt="," + ) + API Reference ------------- .. automodule:: yellowbrick.regressor.influence - :members: CooksDistance + :members: CooksDistance, cooks_distance :undoc-members: :show-inheritance: diff --git a/tests/test_regressor/test_influence.py b/tests/test_regressor/test_influence.py index f07f81054..c3e4b4c21 100644 --- a/tests/test_regressor/test_influence.py +++ b/tests/test_regressor/test_influence.py @@ -122,6 +122,7 @@ def test_cooks_distance_quickmethod(self): draw_threshold=False, linefmt="r-", markerfmt="ro", + show=False ) assert_fitted(viz) diff --git a/yellowbrick/regressor/influence.py b/yellowbrick/regressor/influence.py index 0be2da23b..ee5017ea7 100644 --- a/yellowbrick/regressor/influence.py +++ b/yellowbrick/regressor/influence.py @@ -216,7 +216,7 @@ def finalize(self): def cooks_distance( - X, y, ax=None, draw_threshold=True, linefmt="C0-", markerfmt=",", **kwargs + X, y, ax=None, draw_threshold=True, linefmt="C0-", markerfmt=",", show=True, **kwargs ): """ Cook's Distance is a measure of how influential an instance is to the computation of @@ -266,6 +266,11 @@ def cooks_distance( A string defining the properties of the markers at the stem plot heads. The default is "pixel", e.g. basically no marker head at the top of the stem plot. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however + you cannot call ``plt.savefig`` from this signature, nor + ``clear_figure``. If False, simply calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the final visualization (e.g. size or title parameters). @@ -278,5 +283,12 @@ def cooks_distance( **kwargs ) viz.fit(X, y) - viz.finalize() + + # Draw the final visualization + if show: + viz.show() + else: + viz.finalize() + + # Return the visualizer return viz From d907616f28f6054cf5cf9b3ded89867e8d69cba0 Mon Sep 17 00:00:00 2001 From: mchestnut91 <40185521+mchestnut91@users.noreply.github.com> Date: Sun, 9 Feb 2020 09:21:19 -0500 Subject: [PATCH 35/44] make rank1d quick method primetime (#1033) Updates rank1d quick method to new function signature and verifies that it functions as expected --- docs/api/features/rankd.rst | 27 ++++++++++++++---- .../test_rankd/test_rank1d_quick_method.png | Bin 0 -> 4639 bytes tests/test_features/test_rankd.py | 11 ++++++- yellowbrick/features/rankd.py | 21 +++++++++++--- 4 files changed, 48 insertions(+), 11 deletions(-) create mode 100644 tests/baseline_images/test_features/test_rankd/test_rank1d_quick_method.png diff --git a/docs/api/features/rankd.rst b/docs/api/features/rankd.rst index e47e593e7..842da4c8a 100644 --- a/docs/api/features/rankd.rst +++ b/docs/api/features/rankd.rst @@ -9,7 +9,7 @@ In this example, we'll use the credit default data set from the UCI Machine Lear ================= ============================== Visualizers `Rank1D `_, `Rank2D `_ -Quick Methods `rank2d() `_ +Quick Methods `rank1d() `_, `rank2d() `_ Models General Linear Models Workflow Feature engineering and model selection ================= ============================== @@ -36,7 +36,23 @@ A one-dimensional ranking of features utilizes a ranking algorithm that takes in visualizer.transform(X) # Transform the data visualizer.show() # Finalize and render the figure +Quick Method +------------------------- +The same functionality above can be achieved with the associated quick method ``rank1d``. This method will build the ``Rank1D`` object with the associated arguments, fit it, then (optionally) immediately show it +.. plot:: + :context: close-figs + :alt: rank1d on the credit dataset + + from yellowbrick.features.rankd import rank1d + from yellowbrick.datasets import load_credit + from yellowbrick.features.rankd import rank1d + + #Load the credit dataset + X, y = load_credit() + + fig = rank1d(X, y) + Rank 2D ------- @@ -81,8 +97,8 @@ Alternatively, we can utilize the covariance ranking algorithm, which attempts t visualizer.transform(X) # Transform the data visualizer.show() # Finalize and render the figure -Quick Methods -------------- +Quick Method +------------ Similar functionality as above can be achieved in one line using the associated quick method, ``rank2d``. This method will instantiate and fit a ``Rank2D`` visualizer on the dataset and immediately show it. @@ -98,11 +114,10 @@ Similar functionality as above can be achieved in one line using the associated oz = rank2d(X) - API Reference ------------- .. automodule:: yellowbrick.features.rankd - :members: Rank1D, Rank2D, rank2d + :members: Rank1D, Rank2D, rank1d, rank2d :undoc-members: - :show-inheritance: + :show-inheritance: \ No newline at end of file diff --git a/tests/baseline_images/test_features/test_rankd/test_rank1d_quick_method.png b/tests/baseline_images/test_features/test_rankd/test_rank1d_quick_method.png new file mode 100644 index 0000000000000000000000000000000000000000..d1f0dd060a4f16a4eec743d7eaf654c0c2bb4643 GIT binary patch literal 4639 zcmeHL4^Wb49DWfaEtz_?%xX!uyIH&9%j{BHp*fv;UAk$vD=G=f%Ea&w4G9r$nYpWT zcUy%^lWkpESCp0LOc1Qp6eJ{5^o)5k008Ky$go%dkQe}*K5#+7E9c`iHSmXXdT7)}7xv;>e$q^6K5RB|E-^F=~hdLm_?Kh_WHzZA2F zN=;b}LF4;aO4@FS4gA6Y@MK1Xt=pJU_*mwbN4oEw(-}7>Jmuy)k-n8gUvhT-84u5^ zv!%b*M7F(K>ePf@>=#&CHz$_fR$icO+FulLvdqY--Wh*NA~R))yZ~UcMSL9mV80&* z2mnaVE&zDE9|Et>MK}R))5QY-%qML40zW5b0CGJs0E8U<|I(MLz;}Ecn>9FMVpJ-$ zN|Q<0zXX8Hhh1H#1w!F*ilUm2Q*xBkx(~H!2UsKZEi{or(tm(Yiy+Ir7z4F(lWuhH zXfT9+aGO$l3e@!UIpN%Mnx+ppcjZ{c45 zc0=>Vy40mNmbX^TTBSWA93oK+sa7X7IIC(4u`O2~EW~ry-Mie6xOrayjke1xOg&LF zAV5J=!r2DAkNC8b6o02`|2cj+?vQUm@wua2n{tSyG~Y46kePR8jyDf?yD-05iT_;o zvC2Ppbvce5SO6osHb|Qz^%YC{oTj3;8ZPuB*5I){O2HNmOpCZ@v`CnXV$iPlFfY>x zBW?2ycLi+lSNA?~l^zM&b}@m!whVXh><$$&_bQ!GT?bdHxZhRm)j7?<7WYqLg0D{; zp$BQDXQU5|vx~?li&y0B@Xo1`pDLvj)M#ChEf`$^7^sI@dEz&HlhN(RJf1t|j``8m zr>HUto+};|TUB*ZlC(=|1PhmW^`*&q>v{bCC2Q`l+>o%KeudjX;YUOZgL8a(ZI3>T z>b579UL(0A8b}r;GMxy8j*@+1_|)iNYfhO@Z$STS523o9)2m_1C`HDBMKy1{UNNQd z4Uf{6oqzm8zjxSsTYN$a&9XE+ETjxI#*7v;edB1UEQT{Bx&T8+(88q(rHNHf-%uOc z;P@74157NDe5hQJtzow)ZChkgS;1^0Wm_90^a?CrDlO6NY+iXhb%A8Ln_iP`v9tME z*1d(RIp9|!;a?~}EZyy~pUecQtF-G0428Zx>I)#d{+L`~xhkEm+f$j(>D#w$FOL_~ zd8;__!>+&+#JTEr*D>J+Tr0`0^W9`GS&w#T%_i9K?@`ggdC#gu$ab^=C(^0_aj%ul zSVE$LEwHmdWs(~1Jve%)IDMo>7c45#i z5sgYMPok^MaFnXg57z;p32l2&o6OiaqJ*Vatv~@|x1%2Q()_d~_Nx~EpT}!@l|nlp zjnKI(L@bCk{3L?TVnGxNg?G2}mS%u4XfTwHkB0_>VKKa?lvPl8F@p(>@PHz^hlfYV zWF~46ype$8R^Sd|#{3H&27=`ZMY4%y(KrKod{miKsQ!=sC6F&dPG5rL9d7=K0}oXo NDm*&uV(9iS{{-djR%HMH literal 0 HcmV?d00001 diff --git a/tests/test_features/test_rankd.py b/tests/test_features/test_rankd.py index a72c42a38..a5d6e03ef 100644 --- a/tests/test_features/test_rankd.py +++ b/tests/test_features/test_rankd.py @@ -23,7 +23,7 @@ from tests.base import IS_WINDOWS_OR_CONDA, VisualTestCase -from yellowbrick.datasets import load_occupancy +from yellowbrick.datasets import load_occupancy, load_credit from yellowbrick.features.rankd import * from yellowbrick.features.rankd import kendalltau from yellowbrick.features.rankd import RankDBase @@ -296,6 +296,15 @@ def test_rank1d_integrated_numpy(self): oz.finalize() self.assert_images_similar(oz) + def test_rank1d_quick_method(self): + """ + Test Rank1d quick method + """ + X, y = load_credit() + viz = rank1d(X, y, show=False) + + assert isinstance(viz, Rank1D) + self.assert_images_similar(viz, tol=0.1) ########################################################################## ## Rank2D Test Cases diff --git a/yellowbrick/features/rankd.py b/yellowbrick/features/rankd.py index 804897934..31508d4b7 100644 --- a/yellowbrick/features/rankd.py +++ b/yellowbrick/features/rankd.py @@ -469,7 +469,6 @@ def draw(self, **kwargs): ## Quick Methods ########################################################################## - def rank1d( X, y=None, @@ -479,6 +478,7 @@ def rank1d( orient="h", show_feature_names=True, color=None, + show=True, **kwargs ): """Scores each feature with the algorithm and ranks them in a bar plot. @@ -514,6 +514,15 @@ def rank1d( color: string Specify color for barchart + + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. Returns ------- @@ -533,13 +542,17 @@ def rank1d( ) # Fit and transform the visualizer (calls draw) - visualizer.fit(X, y, **kwargs) + visualizer.fit(X, y) visualizer.transform(X) + if show: + visualizer.show() + else: + visualizer.finalize() + # Return the visualizer object return visualizer - def rank2d( X, y=None, @@ -621,4 +634,4 @@ def rank2d( viz.finalize() # Return the visualizer object - return viz + return viz \ No newline at end of file From 157afa6a46132a55d12acf9a9a8d67efc25fe92a Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Sun, 9 Feb 2020 13:12:43 -0500 Subject: [PATCH 36/44] ClassBalance quick method (#1024) Refactors the class_balance quick method to the new quick method signature and verifies that it works correctly --- docs/api/target/class_balance.rst | 30 +++++++++++++++++- .../test_quick_method_with_splits.png | Bin 0 -> 4147 bytes tests/test_target/test_class_balance.py | 19 +++++++++-- yellowbrick/target/class_balance.py | 20 ++++++++++-- 4 files changed, 63 insertions(+), 6 deletions(-) create mode 100644 tests/baseline_images/test_target/test_class_balance/test_quick_method_with_splits.png diff --git a/docs/api/target/class_balance.rst b/docs/api/target/class_balance.rst index 18507437d..2ec9c9d03 100644 --- a/docs/api/target/class_balance.rst +++ b/docs/api/target/class_balance.rst @@ -7,6 +7,14 @@ One of the biggest challenges for classification models is an imbalance of class There are several techniques for dealing with class imbalance such as stratified sampling, down sampling the majority class, weighting, etc. But before these actions can be taken, it is important to understand what the class balance is in the training data. The ``ClassBalance`` visualizer supports this by creating a bar chart of the *support* for each class, that is the frequency of the classes' representation in the dataset. +================= ============================== +Visualizer :class:`~yellowbrick.target.class_balance.ClassBalance` +Quick Method :func:`~yellowbrick.target.class_balance.class_balance` +Models Classification +Workflow Feature analysis, Target analysis, Model selection +================= ============================== + + .. plot:: :context: close-figs :alt: ClassBalance Visualizer on the game dataset @@ -61,10 +69,30 @@ This visualization allows us to do a quick check to ensure that the proportion o .. note:: This example uses ``TimeSeriesSplit`` to split the data into the training and test sets. For more information on this cross-validation method, please refer to the scikit-learn `documentation `_. + +Quick Method +------------ + +The same functionalities above can be achieved with the associated quick method `class_balance`. This method will build the ``ClassBalance`` object with the associated arguments, fit it, then (optionally) immediately show it. + +.. plot:: + :context: close-figs + :alt: class_balance on the game dataset + + from yellowbrick.datasets import load_game + from yellowbrick.target import class_balance + + # Load the dataset + X, y = load_game() + + # Use the quick method and immediately show the figure + class_balance(y) + + API Reference ------------- .. automodule:: yellowbrick.target.class_balance - :members: ClassBalance + :members: ClassBalance, class_balance :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_target/test_class_balance/test_quick_method_with_splits.png b/tests/baseline_images/test_target/test_class_balance/test_quick_method_with_splits.png new file mode 100644 index 0000000000000000000000000000000000000000..ec9c18b54941ce4bcddd2c93cf9fc24d102ab59c GIT binary patch literal 4147 zcmeHKdrXs86u+e%P=_)lF+R#8nyGUgouJdzwjfa|i}G#L32X-Jup$mAv_;wirJ5=( zfVj979q832HpQ5)BP2mn3^0FNc!o^a-L{KyaRMc_q5ZS{svw)f7% z@H;VWV;m0vUzYQO`!=jH8BVU@Z;0h@;q2mPvbb!pm&H#@;qX(EKMmT)=JJv`siDCu zfb*S)oBDanWFD+MA|6w=gv3TH^Ez~P_YnEU@sS+IM{Sy8 zn;Lz`XO{}s%?YKbYc@e2_vg-|!%cK$!<1|TQ$TJWR! z3F0yK$1*mRG^?T#03*?A%lTR-d~jDyVIsH4xSB-}e|1d(iUUfl3)@3k8oupUN7&eC zPd>$yf&*P!#P)mI7Qc5ioNxdn1po&?hz9|HM(@`E2wFVlN%PwxfV`y+=}bM}5(>bm zlpS|Uh%z%@s5*>H8G&iqb06xb z8*-k3c8;v(U+j$30Kuv|trB{ij13dQQn1-C=uMsit5o-eh&+|2O{nKkvq?arvsxkQ zq*-|b*^D9-mvXtG1&{WbOz9SaB!+h>o{6;6UH;v*WZiLMTXzY}Y9M&U-K0d7|$}W-rin4abe%1Wz~_|!3>ME z85XcmjhRm^i`2yWYp&MU8eD_IDU>H%Dh)h|pH@5R@S}prfKKY-M|eZjD;%8;S2mnY{~*{9EF2ld1f*of{zbpw2AA$pdI9)r}psT@p$B-Hi{ z#!z%j{j{zKDu;BIkF^)U3SskxiS2gSyj2U`%IIk}zHY()^bXsp!Wz@$gQO@gS_2~y zfy^;}C6I{-=H@z5^WADp%{64Te)f2IdG15u#I500;zO%r{Ee)t;!$mDIow=^IeNp2 zP@~jVA8fVLMr^_kYeua>myRj+!S+#zR?6!iEYE7XB%DW=D30sKu9hFnwH4=H3&=;E zGCVO-wKUCuqV0a;Qs>P^^^BK`e;MPK8{sKgcf5)}u=?kyq=Od#e4;i)uRjy9 Date: Sun, 9 Feb 2020 14:23:51 -0500 Subject: [PATCH 37/44] Make validation curve quickmethod primetime (#1030) Refactor the validation_curve quick method to the new signature and verifies that it works correctly --- docs/api/model_selection/validation_curve.rst | 35 ++++++++++++++++++- .../test_validation_curve.py | 3 +- .../model_selection/validation_curve.py | 17 +++++++-- 3 files changed, 51 insertions(+), 4 deletions(-) diff --git a/docs/api/model_selection/validation_curve.rst b/docs/api/model_selection/validation_curve.rst index 4f7e8037a..7a9e19ebd 100644 --- a/docs/api/model_selection/validation_curve.rst +++ b/docs/api/model_selection/validation_curve.rst @@ -7,6 +7,14 @@ Model validation is used to determine how effective an estimator is on data that In order to maximize the score, the hyperparameters of the model must be selected which best allow the model to operate in the specified feature space. Most models have multiple hyperparameters and the best way to choose a combination of those parameters is with a grid search. However, it is sometimes useful to plot the influence of a single hyperparameter on the training and test data to determine if the estimator is underfitting or overfitting for some hyperparameter values. +================= ============================== +Visualizer :class:`~yellowbrick.model_selection.validation_curve.ValidationCurve` +Quick Method :func:`~yellowbrick.model_selection.validation_curve.validation_curve` +Models Classification and Regression +Workflow Model Selection +================= ============================== + + In our first example, we'll explore using the ``ValidationCurve`` visualizer with a regression dataset and in the second, a classification dataset. Note that any estimator that implements ``fit()`` and ``predict()`` and has an appropriate scoring mechanism can be used with this visualizer. .. plot:: @@ -104,10 +112,35 @@ This validation curve poses two possibilities: first, that we do not have the co .. seealso:: This visualizer is based on the validation curve described in the scikit-learn documentation: `Validation Curves `_. The visualizer wraps the `validation_curve `_ function and most of the arguments are passed directly to it. +Quick Method +------------ + +Similar functionality as above can be achieved in one line using the associated quick method, ``validation_curve``. This method will instantiate and fit a ``ValidationCurve`` visualizer. + +.. plot:: + :context: close-figs + :alt: Validation Curve quickmethod for Max Depth of Decision Tree regressor + + import numpy as np + + from yellowbrick.datasets import load_energy + from yellowbrick.model_selection import validation_curve + + from sklearn.tree import DecisionTreeRegressor + + # Load a regression dataset + X, y = load_energy() + + viz = validation_curve( + DecisionTreeRegressor(), X, y, param_name="max_depth", + param_range=np.arange(1, 11), cv=10, scoring="r2", + ) + + API Reference ------------- .. automodule:: yellowbrick.model_selection.validation_curve - :members: ValidationCurve + :members: ValidationCurve, validation_curve :undoc-members: :show-inheritance: diff --git a/tests/test_model_selection/test_validation_curve.py b/tests/test_model_selection/test_validation_curve.py index 664cc3ea0..a3b1af6f9 100644 --- a/tests/test_model_selection/test_validation_curve.py +++ b/tests/test_model_selection/test_validation_curve.py @@ -136,7 +136,8 @@ def test_quick_method(self): pr = np.logspace(-6, -1, 3) cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=321) viz = validation_curve( - SVC(), X, y, logx=True, param_name="gamma", param_range=pr, cv=cv + SVC(), X, y, logx=True, param_name="gamma", + param_range=pr, cv=cv, show=False ) self.assert_images_similar(viz) diff --git a/yellowbrick/model_selection/validation_curve.py b/yellowbrick/model_selection/validation_curve.py index 3d9c47ad8..d2b265db9 100644 --- a/yellowbrick/model_selection/validation_curve.py +++ b/yellowbrick/model_selection/validation_curve.py @@ -306,6 +306,7 @@ def validation_curve( scoring=None, n_jobs=1, pre_dispatch="all", + show=True, **kwargs ): """ @@ -376,6 +377,11 @@ def validation_curve( all). The option can reduce the allocated memory. The string can be an expression like '2*n_jobs'. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however + you cannot call ``plt.savefig`` from this signature, nor + ``clear_figure``. If False, simply calls ``finalize()`` + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. These arguments are @@ -402,7 +408,14 @@ def validation_curve( pre_dispatch=pre_dispatch, ) - # Fit and show the visualizer + # Fit the visualizer oz.fit(X, y) - oz.show(**kwargs) + + # Draw final visualization + if show: + oz.show(**kwargs) + else: + oz.finalize() + + # Return the visualizer object return oz From b9862326764abe6e50ae9b5180b9cfddf34c85cc Mon Sep 17 00:00:00 2001 From: Kristen McIntyre Date: Sun, 9 Feb 2020 15:41:32 -0500 Subject: [PATCH 38/44] Update BalancedBinningReference quick method (#1034) Updates balanced_binning_reference quick method to use new function signature and verifies that it works as expected --- docs/api/target/binning.rst | 27 +++++++++++++++++- .../test_binning/test_quick_method.png | Bin 0 -> 5408 bytes tests/test_target/test_binning.py | 12 ++++++++ yellowbrick/target/binning.py | 13 +++++++-- 4 files changed, 49 insertions(+), 3 deletions(-) create mode 100644 tests/baseline_images/test_target/test_binning/test_quick_method.png diff --git a/docs/api/target/binning.rst b/docs/api/target/binning.rst index ff6733862..a9e55001c 100644 --- a/docs/api/target/binning.rst +++ b/docs/api/target/binning.rst @@ -9,6 +9,12 @@ Sometimes when the your target variable is continuously-valued, there simply are To help the user select the optimal number of bins, the ``BalancedBinningReference`` visualizer takes the target variable ``y`` as input and generates a histogram with vertical lines indicating the recommended value points to ensure that the data is evenly distributed into each bin. +================= ============================== +Visualizer `BalancedBinningReference `_ +Quick Method `balanced_binning_reference `_ +Models Classification +Workflow Feature analysis, Target analysis, Model selection +================= ============================== .. plot:: :context: close-figs @@ -27,6 +33,25 @@ To help the user select the optimal number of bins, the ``BalancedBinningReferen visualizer.show() # Finalize and render the figure +Quick Method +------------ + +The same functionality above can be achieved with the associated quick method ``balanced_binning_reference``. This method will build the ``BalancedBinningReference`` object with the associated arguments, fit it, then (optionally) immediately show it. + +.. plot:: + :context: close-figs + :alt: balanced_binning_reference on the concrete dataset + + from yellowbrick.datasets import load_concrete + from yellowbrick.target import balanced_binning_reference + + # Load the dataset + X, y = load_concrete() + + # Use the quick method and immediately show the figure + balanced_binning_reference(y) + + .. seealso:: To learn more, please read Rebecca Bilbro's article `"Creating Categorical Variables from Continuous Data." `_ @@ -36,6 +61,6 @@ API Reference ------------- .. automodule:: yellowbrick.target.binning - :members: BalancedBinningReference + :members: BalancedBinningReference, balanced_binning_reference :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_target/test_binning/test_quick_method.png b/tests/baseline_images/test_target/test_binning/test_quick_method.png new file mode 100644 index 0000000000000000000000000000000000000000..5030b21d6964bd6349a1ee0df3da7501467cc456 GIT binary patch literal 5408 zcmeHLdrVVj6#qa}WIWt-S`~$i1Vvdps9K@yRs~U!m*R`b!y=`FSc+|VsJAWZ1~`1g zfC4Se6&)6a!jy>?DxE>Y8?pfsQ{ZY!sS167VvDq}TNmBj{@Ek@W3e~6xw+?_^W}W! z{?7T%@0<(|-Rxku*bV^Tuysqo4ghAj0$}ZKI}4Lk#Axnf4;%KztvhY83$~5V!QSVj zY>8w8;6ydwR%H}%A|_nN*|e7v!A#(!Q&}{?pmI_UGC2nm<6P5dEOsI@nTYqo6FptO zh3McEhm}#k2H>s5&aG=^a^I!f^Hc4-lk%Y@)`7Lv+epSyO z%abb{nV44$jn?d9L45-i7u2=-T;$Cp3{69jjcnAUN3m_xw{h(Om~v@V-jMRAd71-# zo_s5bckca5BWZO_SL?OgdQ)k2{TFH&ZhzjepR;3LXPF2{yhFP3+Y{!A9@a2mcS7sZ z0Vq8~-iNB$*|!L(+4a0}cxuklx1g=se`g0GGxgq>Zl$a$%^xH2$J3R){1=TxB!@on za$(+CLvs}`k~gL2M+bQ*A0yMHQd1s91iRm27>lnHjrb9J%7YC0nQ229?YWBY_57qE z+VH`9lm)Nx+WncmGQ%$vQgM7kpc6+#q2e+3@5NOcP<riNe!lXc$C)Qrk=quc{mlsy@7I3 z;J|_d%enBT`M1=A|3y70tTQt@oy?@B!25V6=5)~#gPOpq&hVm4^blG*RgeuuAo~xA zTsqq&Q;bobM#Z5qA8}XW*rM)Xj_-NWvCp{pTP@Zb%8H zAJP>Ji4&{`TcYpVEBWFDH^K0XtTKR$-TU)tGuCDok zLDnCN={ER840m+He70`j9=i`k3(}9N+8(DAV&n;HWD^Bf4z3k8tKjIW<;aFR4~A3Y z!(#Jl7!{MEcb!PO`Hj?(YkD?eoag)ga^|-FzS)n;skoNWjAFRsDMtX$8ut~2Dns^% z#h$3wBF3xD@5H;fQ`JN-drctoI?IbNx>wLzit*ZJm3=g6!v0xLYINdSrV0w&RcjZU z_qJCzBWBcK*bB{s{Lk-bVM$K4m7m9qf900VD(ACEYh$xGyh5F}Z>^IP+vTzy7cHn9 zZoxRwljT}diidF8px+VX zk&6rzB-|8C1f=4!N{qS&C#6PHxjKaS$ILvthR3X(Y(OrMhQ|kqgo15YVj4&26d}C( znoZNk6S~L#p4gbmFv?rvio(y^`!-7=U(aVoDXx}j)<22B)RO9`fuCqwZZE^8ePHXR L(16N~`;Y$ug?!%? literal 0 HcmV?d00001 diff --git a/tests/test_target/test_binning.py b/tests/test_target/test_binning.py index 54ebbb517..93561fb12 100644 --- a/tests/test_target/test_binning.py +++ b/tests/test_target/test_binning.py @@ -61,3 +61,15 @@ def test_pandas_bins(self): visualizer.fit(y) visualizer.finalize() self.assert_images_similar(visualizer, tol=0.5) + + def test_quick_method(self): + """ + Test the quick method with producing a valid visualization + """ + data = load_occupancy(return_dataset=True) + _, y = data.to_numpy() + + visualizer = balanced_binning_reference(y, show=False) + + assert isinstance(visualizer, BalancedBinningReference) + self.assert_images_similar(visualizer, tol=0.5) diff --git a/yellowbrick/target/binning.py b/yellowbrick/target/binning.py index 9ea204be8..44b6450b4 100644 --- a/yellowbrick/target/binning.py +++ b/yellowbrick/target/binning.py @@ -146,7 +146,7 @@ def finalize(self, **kwargs): ########################################################################## -def balanced_binning_reference(y, ax=None, target="y", bins=4, **kwargs): +def balanced_binning_reference(y, ax=None, target="y", bins=4, show=True, **kwargs): """ BalancedBinningReference generates a histogram with vertical lines @@ -166,6 +166,11 @@ def balanced_binning_reference(y, ax=None, target="y", bins=4, **kwargs): bins : number of bins to generate the histogram, default: 4 + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()``. However, you + cannot call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, + simply calls ``finalize()``. + kwargs : dict Keyword arguments that are passed to the base class and may influence the visualization as defined in other Visualizers. @@ -181,6 +186,10 @@ def balanced_binning_reference(y, ax=None, target="y", bins=4, **kwargs): # Fit and show the visualizer visualizer.fit(y) - visualizer.show() + + if show: + visualizer.show() + else: + visualizer.finalize() return visualizer From 8d27323f6326921b99695792b990c47248df438e Mon Sep 17 00:00:00 2001 From: sarthak_jain Date: Mon, 10 Feb 2020 03:32:52 +0530 Subject: [PATCH 39/44] Added quick method for manifold (#1003) Updates manifold quick method with new function signature and ensures that it works as expected --- .../images/concrete_isomap_manifold.png | Bin 81627 -> 81468 bytes .../images/concrete_tsne_manifold.png | Bin 124538 -> 127422 bytes .../features/images/manifold_quick_method.png | Bin 0 -> 80648 bytes ...ccupancy_select_k_best_isomap_manifold.png | Bin 41955 -> 44290 bytes .../images/occupancy_tsne_manifold.png | Bin 43302 -> 45601 bytes docs/api/features/manifold.py | 52 +++++++++++++---- docs/api/features/manifold.rst | 41 +++++++++++-- docs/api/text/postag.rst | 2 +- ...anifold_quick_method_continuous_target.png | Bin 0 -> 95851 bytes ..._manifold_quick_method_discrete_target.png | Bin 0 -> 94524 bytes .../test_manifold_quick_method_no_target.png | Bin 0 -> 36447 bytes tests/test_features/test_manifold.py | 49 ++++++++++++++++ yellowbrick/features/manifold.py | 55 +++++------------- 13 files changed, 144 insertions(+), 55 deletions(-) create mode 100644 docs/api/features/images/manifold_quick_method.png create mode 100644 tests/baseline_images/test_features/test_manifold/test_manifold_quick_method_continuous_target.png create mode 100644 tests/baseline_images/test_features/test_manifold/test_manifold_quick_method_discrete_target.png create mode 100644 tests/baseline_images/test_features/test_manifold/test_manifold_quick_method_no_target.png diff --git 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http://scikit-learn.org/stable/modules/manifold.html#isomap .. _`Multi-Dimensional Scaling`: http://scikit-learn.org/stable/modules/manifold.html#multi-dimensional-scaling-mds @@ -311,7 +311,8 @@ def manifold(self, transformer): # Issue a warning that the n_neighbors was set to a default. warnmsg = ( - "using n_neighbors={}; please explicitly specify for the '{}' manifold" + "using n_neighbors={};" + " please explicitly specify for the '{}' manifold" ).format(self.n_neighbors, str(transformer)) warnings.warn(warnmsg, YellowbrickWarning) @@ -483,6 +484,7 @@ def manifold_embedding( alpha=0.75, random_state=None, colorbar=True, + show=True, **kwargs ): """Quick method for Manifold visualizer. @@ -510,23 +512,10 @@ def manifold_embedding( manifold : str or Transformer, default: "lle" Specify the manifold algorithm to perform the embedding. Either one of - the strings listed in the table below, or an actual scikit-learn + the strings listed in the table above, or an actual scikit-learn transformer. The constructed manifold is accessible with the manifold property, so as to modify hyperparameters before fit. - ============== ========================== - Manifold Description - -------------- -------------------------- - ``"lle"`` `Locally Linear Embedding `_ - ``"ltsa"`` `LTSA LLE `_ - ``"hessian"`` `Hessian LLE `_ - ``"modified"`` `Modified LLE `_ - ``"isomap"`` `Isomap `_ - ``"mds"`` `Multi-Dimensional Scaling `_ - ``"spectral"`` `Spectral Embedding `_ - ``"tsne"`` `t-SNE `_ - ============== ========================== - n_neighbors : int, default: None Many manifold algorithms are nearest neighbors based, for those that are, this parameter specfies the number of neighbors to use in the @@ -587,31 +576,15 @@ def manifold_embedding( If the target_type is "continous" draw a colorbar to the right of the scatter plot. The colobar axes is accessible using the cax property. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments passed to the base class and may influence the feature visualization properties. - Attributes - ---------- - fit_time_ : yellowbrick.utils.timer.Timer - The amount of time in seconds it took to fit the Manifold. - - classes_ : ndarray, shape (n_classes,) - The class labels that define the discrete values in the target. Only - available if the target type is discrete. This is guaranteed to be - strings even if the classes are a different type. - - features_ : ndarray, shape (n_features,) - The names of the features discovered or used in the visualizer that - can be used as an index to access or modify data in X. If a user passes - feature names in, those features are used. Otherwise the columns of a - DataFrame are used or just simply the indices of the data array. - - range_ : (min y, max y) - A tuple that describes the minimum and maximum values in the target. - Only available if the target type is continuous. - - Returns ------- viz : Manifold @@ -634,9 +607,13 @@ def manifold_embedding( **kwargs ) - # Fit and finalize (calls draw) + # Fit and transform the visualizer (calls draw) viz.fit_transform(X, y) - viz.finalize() + + if show: + viz.show() + else: + viz.finalize() # Return the visualizer object return viz From 2f2e5a2534e105f07e2f0db40f67e4e4eb8cff92 Mon Sep 17 00:00:00 2001 From: Kristen McIntyre Date: Sun, 9 Feb 2020 18:19:49 -0500 Subject: [PATCH 40/44] Update Classification Report quick method (#1035) Refactors classification report quick method to new function signature and verifies that it works as expected --- docs/api/classifier/classification_report.rst | 43 ++++++++++- .../test_classification_report.py | 11 ++- .../classifier/classification_report.py | 75 ++++++++++++------- 3 files changed, 98 insertions(+), 31 deletions(-) diff --git a/docs/api/classifier/classification_report.rst b/docs/api/classifier/classification_report.rst index 5eadd88ad..d3c5b9f08 100644 --- a/docs/api/classifier/classification_report.rst +++ b/docs/api/classifier/classification_report.rst @@ -5,6 +5,13 @@ Classification Report The classification report visualizer displays the precision, recall, F1, and support scores for the model. In order to support easier interpretation and problem detection, the report integrates numerical scores with a color-coded heatmap. All heatmaps are in the range ``(0.0, 1.0)`` to facilitate easy comparison of classification models across different classification reports. +================= ================= +Visualizer :class:`~yellowbrick.classifier.classification_report.ClassificationReport` +Quick Method :func:`~yellowbrick.classifier.classification_report.classification_report` +Models Classification +Workflow Model evaluation +================= ================= + .. plot:: :context: close-figs :alt: Classification Report @@ -55,10 +62,44 @@ The metrics are defined in terms of true and false positives, and true and false .. note:: This example uses ``TimeSeriesSplit`` to split the data into the training and test sets. For more information on this cross-validation method, please refer to the scikit-learn `documentation `_. +Quick Method +------------ +The same functionality above can be achieved with the associated quick method ``classification_report``. This method +will build the ``ClassificationReport`` object with the associated arguments, fit it, then (optionally) immediately +show it. + +.. plot:: + :context: close-figs + :alt: classification_report on the occupancy dataset + + from sklearn.model_selection import TimeSeriesSplit + from sklearn.naive_bayes import GaussianNB + + from yellowbrick.datasets import load_occupancy + from yellowbrick.classifier import classification_report + + # Load the classification data set + X, y = load_occupancy() + + # Specify the target classes + classes = ["unoccupied", "occupied"] + + # Create the training and test data + tscv = TimeSeriesSplit() + for train_index, test_index in tscv.split(X): + X_train, X_test = X.iloc[train_index], X.iloc[test_index] + y_train, y_test = y.iloc[train_index], y.iloc[test_index] + + # Instantiate the visualizer + visualizer = classification_report( + GaussianNB(), X_train, y_train, X_test, y_test, classes=classes, support=True + ) + + API Reference ------------- .. automodule:: yellowbrick.classifier.classification_report - :members: ClassificationReport + :members: ClassificationReport, classification_report :undoc-members: :show-inheritance: diff --git a/tests/test_classifier/test_classification_report.py b/tests/test_classifier/test_classification_report.py index cf279a13f..d5e633977 100644 --- a/tests/test_classifier/test_classification_report.py +++ b/tests/test_classifier/test_classification_report.py @@ -203,11 +203,18 @@ def test_quick_method(self): random_state=27, ) + # Create train/test splits + splits = tts(X, y, test_size=0.2, random_state=42) + X_train, X_test, y_train, y_test = splits + _, ax = plt.subplots() model = DecisionTreeClassifier(random_state=19) - classification_report(model, X, y, ax=ax, random_state=42) + visualizer = classification_report( + model, X_train, y_train, X_test, y_test, ax=ax, show=False + ) - self.assert_images_similar(ax=ax, tol=25.0) + assert isinstance(visualizer, ClassificationReport) + self.assert_images_similar(visualizer, tol=12) def test_isclassifier(self): """ diff --git a/yellowbrick/classifier/classification_report.py b/yellowbrick/classifier/classification_report.py index 91b7f3ea8..fbe23137a 100644 --- a/yellowbrick/classifier/classification_report.py +++ b/yellowbrick/classifier/classification_report.py @@ -24,7 +24,6 @@ import numpy as np import matplotlib.pyplot as plt -from sklearn.model_selection import train_test_split from sklearn.metrics import precision_recall_fscore_support from yellowbrick.style import find_text_color @@ -289,17 +288,18 @@ def finalize(self, **kwargs): def classification_report( model, - X, - y, + X_train, + y_train, + X_test=None, + y_test=None, ax=None, - test_size=0.2, - random_state=None, classes=None, cmap="YlOrRd", support=None, encoder=None, is_fitted="auto", force_model=False, + show=True, **kwargs ): """Classification Report @@ -314,23 +314,28 @@ def classification_report( not a classifier, an exception is raised. If the internal model is not fitted, it is fit when the visualizer is fitted, unless otherwise specified by ``is_fitted``. - - X : ndarray or DataFrame of shape n x m - A matrix of n instances with m features. - - y : ndarray or Series of length n - An array or series of target or class values. - + + X_train : ndarray or DataFrame of shape n x m + A feature array of n instances with m features the model is trained on. + Used to fit the visualizer and also to score the visualizer if test splits are + not directly specified. + + y_train : ndarray or Series of length n + An array or series of target or class values. Used to fit the visualizer and + also to score the visualizer if test splits are not specified. + + X_test : ndarray or DataFrame of shape n x m, default: None + An optional feature array of n instances with m features that the model + is scored on if specified, using X_train as the training data. + + y_test : ndarray or Series of length n, default: None + An optional array or series of target or class values that serve as actual + labels for X_test for scoring purposes. + ax : matplotlib Axes, default: None The axes to plot the figure on. If not specified the current axes will be used (or generated if required). - test_size : float, default=0.2 - The percentage of the data to reserve as test data. - - random_state : int or None, default=None - The value to seed the random number generator for shuffling data. - classes : list of str, defult: None The class labels to use for the legend ordered by the index of the sorted classes discovered in the ``fit()`` method. Specifying classes in this @@ -353,10 +358,10 @@ def classification_report( passed to ``fit()`` or ``score()``. The encoder disambiguates this mismatch ensuring that classes are labeled correctly in the visualization. - is_fitted : bool or str, default="auto" + is_fitted : bool or str, default='auto' Specify if the wrapped estimator is already fitted. If False, the estimator will be fit when the visualizer is fit, otherwise, the estimator will not be - modified. If "auto" (default), a helper method will check if the estimator + modified. If 'auto' (default), a helper method will check if the estimator is fitted before fitting it again. force_model : bool, default: False @@ -364,6 +369,11 @@ def classification_report( will prevent an exception when the visualizer is initialized but may result in unexpected or unintended behavior. + show: bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + kwargs : dict Keyword arguments passed to the visualizer base classes. @@ -385,15 +395,24 @@ def classification_report( **kwargs ) - # Create the train and test splits - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=test_size, random_state=random_state - ) - # Fit and transform the visualizer (calls draw) - visualizer.fit(X_train, y_train, **kwargs) - visualizer.score(X_test, y_test) - visualizer.finalize() + visualizer.fit(X_train, y_train) + + # Score the visualizer + if X_test is not None and y_test is not None: + visualizer.score(X_test, y_test) + elif X_test is not None or y_test is not None: + raise YellowbrickValueError( + "both X_test and y_test are required if one is specified" + ) + else: + visualizer.score(X_train, y_train) + + # Draw the final visualization + if show: + visualizer.show() + else: + visualizer.finalize() # Return the visualizer return visualizer From 8e7093c1d0bca655314a375a22d86df9681fb493 Mon Sep 17 00:00:00 2001 From: pdamodaran Date: Tue, 11 Feb 2020 09:12:43 -0500 Subject: [PATCH 41/44] Added quick method for JointPlotVisualizer (#1032) --- docs/api/features/jointplot.rst | 27 +++- .../test_jointplot/test_quick_method.png | Bin 0 -> 19804 bytes tests/test_features/test_jointplot.py | 14 +++ yellowbrick/features/jointplot.py | 119 +++++++++++++++++- 4 files changed, 157 insertions(+), 3 deletions(-) create mode 100644 tests/baseline_images/test_features/test_jointplot/test_quick_method.png diff --git a/docs/api/features/jointplot.rst b/docs/api/features/jointplot.rst index a0cb45b21..184509169 100644 --- a/docs/api/features/jointplot.rst +++ b/docs/api/features/jointplot.rst @@ -10,6 +10,12 @@ Joint Plot Visualization The ``JointPlotVisualizer`` plots a feature against the target and shows the distribution of each via a histogram on each axis. +================= ============================== +Visualizer :class:`~yellowbrick.features.jointplot.JointPlot` +Quick Method :func:`~yellowbrick.features.jointplot.joint_plot` +Models Classification/Regression +Workflow Feature Engineering/Selection +================= ============================== .. plot:: :context: close-figs @@ -66,11 +72,30 @@ of many, many points. visualizer.fit_transform(X, y) # Fit and transform the data visualizer.show() # Finalize and render the figure +Quick Method +---------------------- +The same functionality above can be achieved with the associated quick method ``joint_plot``. This method +will build the ``JointPlot`` object with the associated arguments, fit it, then (optionally) immediately +show it. + +.. plot:: + :context: close-figs + :alt: JointPlot + + from yellowbrick.datasets import load_concrete + from yellowbrick.features import joint_plot + + # Load the dataset + X, y = load_concrete() + + # Instantiate the visualizer + visualizer = joint_plot(X, y, columns="cement") + API Reference ------------- .. automodule:: yellowbrick.features.jointplot - :members: JointPlot + :members: JointPlot, joint_plot :undoc-members: :show-inheritance: diff --git a/tests/baseline_images/test_features/test_jointplot/test_quick_method.png b/tests/baseline_images/test_features/test_jointplot/test_quick_method.png new file mode 100644 index 0000000000000000000000000000000000000000..0a97f722f92a86504a91ee9c2b19c5e6da1c1e18 GIT binary patch literal 19804 zcmdtK2T)YY_BXmg5fl{^35o_i*Bj40_uhYc-+N!xt5@$-ox%;hyL+u(y~1y;wckEcet6;BwQ~>zU67NN 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z7@I@Y3Z2Ah_xAfhkdp|v|1ahFzZ9MSPWb=7I`{y3BhI0ht)!m`sD|VoC`)HcJ$~`O E00nkqJOBUy literal 0 HcmV?d00001 diff --git a/tests/test_features/test_jointplot.py b/tests/test_features/test_jointplot.py index aba077396..3f21f821d 100644 --- a/tests/test_features/test_jointplot.py +++ b/tests/test_features/test_jointplot.py @@ -492,3 +492,17 @@ def test_columns_double_index_continuous_y_hist(self): oz.finalize() # Appveyor and Linux conda failed due to non-text-based differences self.assert_images_similar(oz, tol=4.0) + + @pytest.mark.xfail( + IS_WINDOWS_OR_CONDA, + reason="font rendering different in OS and/or Python; see #892", + ) + def test_quick_method(self): + """ + Test the joint_plot quick method + """ + oz = joint_plot(self.continuous.X, self.continuous.y, columns=1, show=False) + assert isinstance(oz, JointPlot) + assert hasattr(oz, "corr_") + + self.assert_images_similar(oz) diff --git a/yellowbrick/features/jointplot.py b/yellowbrick/features/jointplot.py index bebacc557..50d1c1567 100644 --- a/yellowbrick/features/jointplot.py +++ b/yellowbrick/features/jointplot.py @@ -447,5 +447,120 @@ def _index_into(self, idx, data): ########################################################################## -def joint_plot(): - raise NotImplementedError("quick method still needs to be implemented") +def joint_plot( + X, + y, + ax=None, + columns=None, + correlation="pearson", + kind="scatter", + hist=True, + alpha=0.65, + joint_kws=None, + hist_kws=None, + show=True, + **kwargs +): + """ + Joint plots are useful for machine learning on multi-dimensional data, allowing for + the visualization of complex interactions between different data dimensions, their + varying distributions, and even their relationships to the target variable for + prediction. + + The Yellowbrick ``JointPlot`` can be used both for pairwise feature analysis and + feature-to-target plots. For pairwise feature analysis, the ``columns`` argument can + be used to specify the index of the two desired columns in ``X``. If ``y`` is also + specified, the plot can be colored with a heatmap or by class. For feature-to-target + plots, the user can provide either ``X`` and ``y`` as 1D vectors, or a ``columns`` + argument with an index to a single feature in ``X`` to be plotted against ``y``. + + Histograms can be included by setting the ``hist`` argument to ``True`` for a + frequency distribution, or to ``"density"`` for a probability density function. Note + that histograms requires matplotlib 2.0.2 or greater. + + Parameters + ---------- + X : array-like + An array-like object of either 1 or 2 dimensions depending on self.columns. + Usually this is a 2D table with shape (n, m) + + y : array-like, default: None + An vector or 1D array that has the same length as X. May be used to either + directly plot data or to color data points. + + ax : matplotlib Axes, default: None + The axes to plot the figure on. If None is passed in the current axes will be + used (or generated if required). This is considered the base axes where the + the primary joint plot is drawn. It will be shifted and two additional axes + added above (xhax) and to the right (yhax) if hist=True. + + columns : int, str, [int, int], [str, str], default: None + Determines what data is plotted in the joint plot and acts as a selection index + into the data passed to ``fit(X, y)``. This data therefore must be indexable by + the column type (e.g. an int for a numpy array or a string for a DataFrame). + + If None is specified then either both X and y must be 1D vectors and they will + be plotted against each other or X must be a 2D array with only 2 columns. If a + single index is specified then the data is indexed as ``X[columns]`` and plotted + jointly with the target variable, y. If two indices are specified then they are + both selected from X, additionally in this case, if y is specified, then it is + used to plot the color of points. + + Note that these names are also used as the x and y axes labels if they aren't + specified in the joint_kws argument. + + correlation : str, default: 'pearson' + The algorithm used to compute the relationship between the variables in the + joint plot, one of: 'pearson', 'covariance', 'spearman', 'kendalltau'. + + kind : str in {'scatter', 'hex'}, default: 'scatter' + The type of plot to render in the joint axes. Note that when kind='hex' the + target cannot be plotted by color. + + hist : {True, False, None, 'density', 'frequency'}, default: True + Draw histograms showing the distribution of the variables plotted jointly. + If set to 'density', the probability density function will be plotted. + If set to True or 'frequency' then the frequency will be plotted. + Requires Matplotlib >= 2.0.2. + + alpha : float, default: 0.65 + Specify a transparency where 1 is completely opaque and 0 is completely + transparent. This property makes densely clustered points more visible. + + {joint, hist}_kws : dict, default: None + Additional keyword arguments for the plot components. + + show : bool, default: True + If True, calls ``show()``, which in turn calls ``plt.show()`` however you cannot + call ``plt.savefig`` from this signature, nor ``clear_figure``. If False, simply + calls ``finalize()`` + + kwargs : dict + Keyword arguments that are passed to the base class and may influence + the visualization as defined in other Visualizers. + + Attributes + ---------- + corr_ : float + The correlation or relationship of the data in the joint plot, specified by the + correlation algorithm. + """ + visualizer = JointPlot( + ax=ax, + columns=columns, + correlation=correlation, + kind=kind, + hist=hist, + alpha=alpha, + joint_kws=joint_kws, + hist_kws=hist_kws, + **kwargs + ) + + # Fit and show the visualizer + visualizer.fit(X, y) + if show: + visualizer.show() + else: + visualizer.finalize() + return visualizer From 33f1b962b279c4d39302c24bef6e3fb79a3f2f01 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Wed, 12 Feb 2020 18:47:42 -0500 Subject: [PATCH 42/44] Minor documentation fixes (#1036) Minor documentation fixes and links as well as two actual bugs in the quick methods for umap and pca --- docs/api/classifier/confusion_matrix.rst | 9 ++-- docs/api/classifier/prcurve.rst | 4 +- docs/api/classifier/rocauc.rst | 8 ++-- docs/api/classifier/threshold.rst | 10 +++-- docs/api/cluster/elbow.rst | 7 +-- docs/api/cluster/silhouette.rst | 4 +- docs/api/features/manifold.rst | 7 +-- docs/api/features/pca.rst | 43 +++++++++--------- docs/api/features/pcoords.rst | 8 ++-- docs/api/features/radviz.rst | 9 ++-- docs/api/features/rankd.rst | 44 +++++++------------ docs/api/model_selection/cross_validation.rst | 5 ++- docs/api/model_selection/rfecv.rst | 7 +-- docs/api/target/binning.rst | 4 +- docs/api/target/feature_correlation.rst | 10 +++-- docs/api/text/dispersion.rst | 8 ++-- docs/api/text/postag.rst | 17 +++++-- docs/api/text/tsne.rst | 8 ++-- docs/api/text/umap_vis.rst | 8 ++-- yellowbrick/features/pca.py | 1 + yellowbrick/features/pcoords.py | 10 +---- yellowbrick/text/umap_vis.py | 4 +- 22 files changed, 124 insertions(+), 111 deletions(-) diff --git a/docs/api/classifier/confusion_matrix.rst b/docs/api/classifier/confusion_matrix.rst index 6b907363b..e8ea0cb9c 100644 --- a/docs/api/classifier/confusion_matrix.rst +++ b/docs/api/classifier/confusion_matrix.rst @@ -17,8 +17,8 @@ information can be found by looking at the scikit-learn documentation on `confusion matrices `_. ================= ================= -Visualizer `ConfusionMatrix `_ -Quick Method `confusion_matrix() `_ +Visualizer :class:`~yellowbrick.classifier.confusion_matrix.ConfusionMatrix` +Quick Method :func:`~yellowbrick.classifier.confusion_matrix.confusion_matrix` Models Classification Workflow Model evaluation ================= ================= @@ -92,7 +92,8 @@ Class names can be added to a ``ConfusionMatrix`` plot using the ``label_encoder iris_cm.show() Quick Method -------------------------- +------------ + The same functionality above can be achieved with the associated quick method ``confusion_matrix``. This method will build the ``ConfusionMatrix`` object with the associated arguments, fit it, then (optionally) immediately show it. In the below example we can see how a ``LogisticRegression`` struggles to effectively model the credit dataset (hint: check out :class:`~yellowbrick.features.rankd.Rank2D` to examine for multicollinearity!). .. plot:: @@ -116,6 +117,8 @@ The same functionality above can be achieved with the associated quick method `` X_train, y_train, X_test, y_test, classes=['not_defaulted', 'defaulted'] ) + plt.tight_layout() + API Reference ------------- diff --git a/docs/api/classifier/prcurve.rst b/docs/api/classifier/prcurve.rst index d456aa688..c6afc87fa 100644 --- a/docs/api/classifier/prcurve.rst +++ b/docs/api/classifier/prcurve.rst @@ -12,8 +12,8 @@ scenario for a classifier, showing a model that returns accurate results for the majority of classes it selects. ================= ============================== -Visualizer `PrecisionRecallCurve `_ -Quick Method `precision_recall_curve() `_ +Visualizer :class:`~yellowbrick.classifier.prcurve.PrecisionRecallCurve` +Quick Method :func:`~yellowbrick.classifier.prcurve.precision_recall_curve` Models Classification Workflow Model evaluation ================= ============================== diff --git a/docs/api/classifier/rocauc.rst b/docs/api/classifier/rocauc.rst index 3bb0d5642..81539af83 100644 --- a/docs/api/classifier/rocauc.rst +++ b/docs/api/classifier/rocauc.rst @@ -10,8 +10,8 @@ The Receiver Operating Characteristic (ROC) is a measure of a classifier's predi This leads to another metric, area under the curve (AUC), which is a computation of the relationship between false positives and true positives. The higher the AUC, the better the model generally is. However, it is also important to inspect the "steepness" of the curve, as this describes the maximization of the true positive rate while minimizing the false positive rate. ================= ================= -Visualizer `ROCAUC `_ -Quick Method `roc_auc() `_ +Visualizer :class:`~yellowbrick.classifier.rocauc.ROCAUC` +Quick Method :func:`~yellowbrick.classifier.rocauc.roc_auc` Models Classification Workflow Model evaluation ================= ================= @@ -90,7 +90,7 @@ ROC curves are typically used in binary classification, and in fact the Scikit-L By default with multi-class ROCAUC visualizations, a curve for each class is plotted, in addition to the micro- and macro-average curves for each class. This enables the user to inspect the tradeoff between sensitivity and specificity on a per-class basis. Note that for multi-class ``ROCAUC``, at least one of the ``micro``, ``macro``, or ``per_class`` parameters must be set to ``True`` (by default, all are set to ``True``). Quick Method -------------------------- +------------ The same functionality above can be achieved with the associated quick method ``roc_auc``. This method will build the ``ROCAUC`` object with the associated arguments, fit it, then (optionally) immediately show it .. plot:: @@ -100,7 +100,7 @@ The same functionality above can be achieved with the associated quick method `` from yellowbrick.classifier.rocauc import roc_auc from yellowbrick.datasets import load_credit from sklearn.linear_model import LogisticRegression - from sklearn.model_selection import train_test_split + from sklearn.model_selection import train_test_split #Load the classification dataset X, y = load_credit() diff --git a/docs/api/classifier/threshold.rst b/docs/api/classifier/threshold.rst index 23454ca8d..661a9b3ac 100644 --- a/docs/api/classifier/threshold.rst +++ b/docs/api/classifier/threshold.rst @@ -8,8 +8,8 @@ Discrimination Threshold A visualization of precision, recall, f1 score, and queue rate with respect to the discrimination threshold of a binary classifier. The *discrimination threshold* is the probability or score at which the positive class is chosen over the negative class. Generally, this is set to 50% but the threshold can be adjusted to increase or decrease the sensitivity to false positives or to other application factors. ================= ================= -Visualizer `DiscriminationThreshold `_ -Quick Method `discrimination_threshold() `_ +Visualizer :class:`~yellowbrick.classifier.threshold.DiscriminationThreshold` +Quick Method :func:`~yellowbrick.classifier.threshold.discrimination_threshold` Models Classification Workflow Model evaluation ================= ================= @@ -50,7 +50,8 @@ Generally speaking, the threshold is balanced between cases and set to 0.5 or 50 In the figure above we see the visualizer tuned to look for the optimal F1 score, which is annotated as a threshold of 0.43. The model is run multiple times over multiple train/test splits in order to account for the variability of the model with respect to the metrics (shown as the fill area around the median curve). Quick Method -------------------------- +------------ + The same functionality above can be achieved with the associated quick method ``discrimination_threshold``. This method will build the ``DiscriminationThreshold`` object with the associated arguments, fit it, then (optionally) immediately show it .. plot:: @@ -66,9 +67,10 @@ The same functionality above can be achieved with the associated quick method `` # Instantiate the visualizer with the classification model model = KNeighborsClassifier(3) - + discrimination_threshold(model, X, y) + API Reference ------------- diff --git a/docs/api/cluster/elbow.rst b/docs/api/cluster/elbow.rst index eef5badd1..5ae808c78 100644 --- a/docs/api/cluster/elbow.rst +++ b/docs/api/cluster/elbow.rst @@ -8,8 +8,8 @@ The ``KElbowVisualizer`` implements the "elbow" method to help data scientists s To demonstrate, in the following example the ``KElbowVisualizer`` fits the ``KMeans`` model for a range of :math:`K` values from 4 to 11 on a sample two-dimensional dataset with 8 random clusters of points. When the model is fit with 8 clusters, we can see a line annotating the "elbow" in the graph, which in this case we know to be the optimal number. ================= ============================== -Visualizer `KElbowVisualizer `_ -Quick Method `kelbow_visualizer() `_ +Visualizer :class:`~yellowbrick.cluster.elbow.KElbowVisualizer` +Quick Method :func:`~yellowbrick.cluster.elbow.kelbow_visualizer` Models Clustering Workflow Model evaluation ================= ============================== @@ -89,7 +89,8 @@ It is important to remember that the "elbow" method does not work well if the da is not very clustered. In this case, you might see a smooth curve and the optimal value of :math:`K` will be unclear. Quick Method -------------------------- +------------ + The same functionality above can be achieved with the associated quick method ``kelbow_visualizer``. This method will build the ``KElbowVisualizer`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. .. plot:: diff --git a/docs/api/cluster/silhouette.rst b/docs/api/cluster/silhouette.rst index 232da7cec..3431da8e2 100644 --- a/docs/api/cluster/silhouette.rst +++ b/docs/api/cluster/silhouette.rst @@ -8,8 +8,8 @@ The Silhouette Coefficient is used when the ground-truth about the dataset is un The Silhouette Visualizer displays the silhouette coefficient for each sample on a per-cluster basis, visualizing which clusters are dense and which are not. This is particularly useful for determining cluster imbalance, or for selecting a value for :math:`K` by comparing multiple visualizers. ================= ============================== -Visualizer `SilhouetteVisualizer `_ -Quick Method `silhouette_visualizer() `_ +Visualizer :class:`~yellowbrick.cluster.silhouette.SilhouetteVisualizer` +Quick Method :func:`~yellowbrick.cluster.silhouette.silhouette_visualizer` Models Clustering Workflow Model evaluation ================= ============================== diff --git a/docs/api/features/manifold.rst b/docs/api/features/manifold.rst index b86a5bd34..dafe3caeb 100644 --- a/docs/api/features/manifold.rst +++ b/docs/api/features/manifold.rst @@ -15,8 +15,8 @@ to create a decision space in the data. ================= ================= -Visualizer `Manifold `_ -Quick Method `manifold_embedding() `_ +Visualizer :class:`~yellowbrick.features.manifold.Manifold` +Quick Method :func:`~yellowbrick.features.manifold.manifold_embedding` Models Classification, Regression Workflow Feature Engineering ================= ================= @@ -154,7 +154,8 @@ continuous by counting the number of unique values in ``y``. Quick Method ------------------ +------------ + The same functionality above can be achieved with the associated quick method ``manifold_embedding``. This method will build the ``Manifold`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. .. note to contributors: the below code takes a long time to run so has not been diff --git a/docs/api/features/pca.rst b/docs/api/features/pca.rst index 9d512fa1d..dc12459a3 100644 --- a/docs/api/features/pca.rst +++ b/docs/api/features/pca.rst @@ -6,8 +6,8 @@ PCA Projection The PCA Decomposition visualizer utilizes principal component analysis to decompose high dimensional data into two or three dimensions so that each instance can be plotted in a scatter plot. The use of PCA means that the projected dataset can be analyzed along axes of principal variation and can be interpreted to determine if spherical distance metrics can be utilized. ================= ================= -Visualizer `PCA Decomposition `_ -Quick Method `pca_decomposition `_ +Visualizer :class:`~yellowbrick.features.pca.PCA` +Quick Method :func:`~yellowbrick.features.pca.pca_decomposition` Models Classification/Regression Workflow Feature Engineering/Selection ================= ================= @@ -17,15 +17,13 @@ Workflow Feature Engineering/Selection :alt: PCA Projection, 2D from yellowbrick.datasets import load_credit - from yellowbrick.features import PCADecomposition + from yellowbrick.features import PCA # Specify the features of interest and the target X, y = load_credit() + classes = ['account in default', 'current with bills'] - # Create a list of colors to assign to points in the plot - colors = np.array(['r' if yi else 'b' for yi in y]) - - visualizer = PCADecomposition(scale=True, color=colors) + visualizer = PCA(scale=True, classes=classes) visualizer.fit_transform(X, y) visualizer.show() @@ -37,13 +35,14 @@ The PCA projection can also be plotted in three dimensions to attempt to visuali :alt: PCA Projection, 3D from yellowbrick.datasets import load_credit - from yellowbrick.features import PCADecomposition + from yellowbrick.features import PCA X, y = load_credit() + classes = ['account in default', 'current with bills'] - colors = np.array(['r' if yi else 'b' for yi in y]) - - visualizer = PCADecomposition(scale=True, color=colors, projection=3) + visualizer = PCA( + scale=True, projection=3, classes=classes + ) visualizer.fit_transform(X, y) visualizer.show() @@ -58,12 +57,12 @@ The PCA projection can be enhanced to a biplot whose points are the projected in :alt: PCA biplot projection, 2D from yellowbrick.datasets import load_concrete - from yellowbrick.features import PCADecomposition + from yellowbrick.features import PCA # Load the concrete dataset X, y = load_concrete() - visualizer = PCADecomposition(scale=True, proj_features=True) + visualizer = PCA(scale=True, proj_features=True) visualizer.fit_transform(X, y) visualizer.show() @@ -73,18 +72,19 @@ The PCA projection can be enhanced to a biplot whose points are the projected in :alt: PCA biplot projection, 3D from yellowbrick.datasets import load_concrete - from yellowbrick.features import PCADecomposition + from yellowbrick.features import PCA X, y = load_concrete() - visualizer = PCADecomposition(scale=True, proj_features=True, projection=3) + visualizer = PCA(scale=True, proj_features=True, projection=3) visualizer.fit_transform(X, y) visualizer.show() Quick Method ----------------------- +------------ + The same functionality above can be achieved with the associated quick method ``pca_decomposition``. This method -will build the ``PCADecomposition`` object with the associated arguments, fit it, then (optionally) immediately +will build the ``PCA`` object with the associated arguments, fit it, then (optionally) immediately show it. .. plot:: @@ -96,12 +96,13 @@ show it. # Specify the features of interest and the target X, y = load_credit() + classes = ['account in default', 'current with bills'] - # Create a list of colors to assign to points in the plot - colors = np.array(['r' if yi else 'b' for yi in y]) + # Create, fit, and show the visualizer + pca_decomposition( + X, y, scale=True, classes=classes + ) - # Instantiate the visualizer - visualizer = pca_decomposition(X, y, scale=True, color=colors) API Reference ------------- diff --git a/docs/api/features/pcoords.rst b/docs/api/features/pcoords.rst index 5c7464d00..56f979b55 100644 --- a/docs/api/features/pcoords.rst +++ b/docs/api/features/pcoords.rst @@ -8,8 +8,8 @@ Parallel coordinates is multi-dimensional feature visualization technique where Data scientists use this method to detect clusters of instances that have similar classes, and to note features that have high variance or different distributions. We can see this in action after first loading our occupancy classification dataset. ================= ================= -Visualizer `Parallel Coordinates `_ -Quick Method `parallel_coordinates `_ +Visualizer :class:`~yellowbrick.features.pcoords.ParallelCoordinates` +Quick Method :func:`~yellowbrick.features.pcoords.parallel_coordinates` Models Classification Workflow Feature analysis ================= ================= @@ -115,7 +115,8 @@ As you can see the "fast" drawing algorithm does not have the same build up of c .. image:: images/fast_parallel_coordinates_speedup.png Quick Method ----------------------- +------------ + The same functionality above can be achieved with the associated quick method ``parallel_coordinates``. This method will build the ``ParallelCoordinates`` object with the associated arguments, fit it, then (optionally) immediately show it. @@ -139,6 +140,7 @@ show it. # Instantiate the visualizer visualizer = parallel_coordinates(X, y, classes=classes, features=features) + API Reference ------------- diff --git a/docs/api/features/radviz.rst b/docs/api/features/radviz.rst index 1a2a31c0d..bdb6f513d 100644 --- a/docs/api/features/radviz.rst +++ b/docs/api/features/radviz.rst @@ -23,8 +23,8 @@ If you do receive this warning, you may want to look at imputation strategies. A good starting place is the `scikit-learn Imputer. `_ ================= ================= -Visualizer `RadViz `_ -Quick Method `radviz() `_ +Visualizer :class:`~yellowbrick.features.radviz.RadialVisualizer` +Quick Method :func:`~yellowbrick.features.radviz.radviz` Models Classification, Regression Workflow Feature Engineering ================= ================= @@ -54,7 +54,8 @@ display the target information, as opposed to discrete colors. Quick Method -------------------------- +------------ + The same functionality above can be achieved with the associated quick method ``radviz``. This method will build the ``RadViz`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. .. plot:: @@ -63,7 +64,7 @@ The same functionality above can be achieved with the associated quick method `` from yellowbrick.features.radviz import radviz from yellowbrick.datasets import load_occupancy - + #Load the classification dataset X, y = load_occupancy() diff --git a/docs/api/features/rankd.rst b/docs/api/features/rankd.rst index 842da4c8a..29a43a891 100644 --- a/docs/api/features/rankd.rst +++ b/docs/api/features/rankd.rst @@ -8,8 +8,8 @@ Rank Features In this example, we'll use the credit default data set from the UCI Machine Learning repository to rank features. The code below creates our instance matrix and target vector. ================= ============================== -Visualizers `Rank1D `_, `Rank2D `_ -Quick Methods `rank1d() `_, `rank2d() `_ +Visualizers :class:`~yellowbrick.features.rankd.Rank1D`, :class:`~yellowbrick.features.rankd.Rank2D` +Quick Methods :func:`~yellowbrick.features.rankd.rank1d`, :func:`~yellowbrick.features.rankd.rank2d` Models General Linear Models Workflow Feature engineering and model selection ================= ============================== @@ -36,23 +36,6 @@ A one-dimensional ranking of features utilizes a ranking algorithm that takes in visualizer.transform(X) # Transform the data visualizer.show() # Finalize and render the figure -Quick Method -------------------------- -The same functionality above can be achieved with the associated quick method ``rank1d``. This method will build the ``Rank1D`` object with the associated arguments, fit it, then (optionally) immediately show it - -.. plot:: - :context: close-figs - :alt: rank1d on the credit dataset - - from yellowbrick.features.rankd import rank1d - from yellowbrick.datasets import load_credit - from yellowbrick.features.rankd import rank1d - - #Load the credit dataset - X, y = load_credit() - - fig = rank1d(X, y) - Rank 2D ------- @@ -97,22 +80,27 @@ Alternatively, we can utilize the covariance ranking algorithm, which attempts t visualizer.transform(X) # Transform the data visualizer.show() # Finalize and render the figure -Quick Method ------------- +Quick Methods +------------- -Similar functionality as above can be achieved in one line using the associated quick method, ``rank2d``. This method will instantiate and fit a ``Rank2D`` visualizer on the dataset and immediately show it. +Similar functionality can be achieved using the one line quick methods, ``rank1d`` and ``rank2d``. These functions instantiate and fit their respective visualizer on the data and immediately show it without having to use the class-based API. .. plot:: :context: close-figs :alt: rank2d quick method on credit dataset with pearson algorithm - from yellowbrick.datasets import load_credit - from yellowbrick.features import rank2d + from yellowbrick.datasets import load_concrete + from yellowbrick.features import rank1d, rank2d - # Load the credit dataset - X, _ = load_credit() + # Load the concrete dataset + X, _ = load_concrete() + + _, axes = plt.subplots(ncols=2, figsize=(8,4)) + + rank1d(X, ax=axes[0], show=False) + rank2d(X, ax=axes[1], show=False) + plt.show() - oz = rank2d(X) API Reference ------------- @@ -120,4 +108,4 @@ API Reference .. automodule:: yellowbrick.features.rankd :members: Rank1D, Rank2D, rank1d, rank2d :undoc-members: - :show-inheritance: \ No newline at end of file + :show-inheritance: diff --git a/docs/api/model_selection/cross_validation.rst b/docs/api/model_selection/cross_validation.rst index ee60a29ad..2d3288daa 100644 --- a/docs/api/model_selection/cross_validation.rst +++ b/docs/api/model_selection/cross_validation.rst @@ -4,8 +4,8 @@ Cross Validation Scores ======================= ================= ===================== - Visualizer `CVScores `_ - Quick Method `cv_scores() `_ + Visualizer :class:`~yellowbrick.model_selection.cross_validation.CVScores` + Quick Method :func:`~yellowbrick.model_selection.cross_validation.cv_scores` Models Classification, Regression Workflow Model Selection ================= ===================== @@ -85,6 +85,7 @@ As with our classification ``CVScores`` visualization, our regression visualizat Quick Method ------------ + The same functionality above can be achieved with the associated quick method ``cv_scores``. This method will build the ``CVScores`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. .. plot:: diff --git a/docs/api/model_selection/rfecv.rst b/docs/api/model_selection/rfecv.rst index c6e36ea41..5b7fd1dda 100644 --- a/docs/api/model_selection/rfecv.rst +++ b/docs/api/model_selection/rfecv.rst @@ -4,8 +4,8 @@ Recursive Feature Elimination ============================= ================= ===================== - Visualizer `RFECV `_ - Quick Method `rfecv() `_ + Visualizer :class:`~yellowbrick.model_selection.rfecv.RFECV` + Quick Method :func:`~yellowbrick.model_selection.rfecv.rfecv` Models Classification, Regression Workflow Model Selection ================= ===================== @@ -71,7 +71,8 @@ In this example we can see that 19 features were selected, though there doesn't .. seealso:: This visualizer is is based off of the visualization in the scikit-learn documentation: `recursive feature elimination with cross-validation `_. However, the Yellowbrick version does not use ``sklearn.feature_selection.RFECV`` but instead wraps ``sklearn.feature_selection.RFE`` models. The fitted model can be accessed on the visualizer using the ``viz.rfe_estimator_`` attribute, and in fact the visualizer acts as the fitted model when using ``predict()`` or ``score()``. Quick Method -------------------------- +------------ + The same functionality above can be achieved with the associated quick method ``rfecv``. This method will build the ``RFECV`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. .. code:: python diff --git a/docs/api/target/binning.rst b/docs/api/target/binning.rst index a9e55001c..0caf5d1ee 100644 --- a/docs/api/target/binning.rst +++ b/docs/api/target/binning.rst @@ -10,8 +10,8 @@ Sometimes when the your target variable is continuously-valued, there simply are To help the user select the optimal number of bins, the ``BalancedBinningReference`` visualizer takes the target variable ``y`` as input and generates a histogram with vertical lines indicating the recommended value points to ensure that the data is evenly distributed into each bin. ================= ============================== -Visualizer `BalancedBinningReference `_ -Quick Method `balanced_binning_reference `_ +Visualizer :class:`~yellowbrick.target.binning.BalancedBinningReference` +Quick Method :func:`~yellowbrick.target.binning.balanced_binning_reference` Models Classification Workflow Feature analysis, Target analysis, Model selection ================= ============================== diff --git a/docs/api/target/feature_correlation.rst b/docs/api/target/feature_correlation.rst index 8f1c944e3..0f521a3f1 100644 --- a/docs/api/target/feature_correlation.rst +++ b/docs/api/target/feature_correlation.rst @@ -12,8 +12,8 @@ Pearson Correlation The default calculation is Pearson correlation, which is performed with ``scipy.stats.pearsonr``. ================= ================= -Visualizer `FeatureCorrelation `_ -Quick Method `feature_correlation() `_ +Visualizer :class:`~yellowbrick.target.feature_correlation.FeatureCorrelation` +Quick Method :func:`~yellowbrick.target.feature_correlation.feature_correlation` Models Regression/Classification/Clustering Workflow Feature Engineering/Model Selection ================= ================= @@ -38,6 +38,7 @@ Workflow Feature Engineering/Model Selection visualizer.fit(X, y) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure + Mutual Information - Regression ------------------------------- @@ -101,8 +102,10 @@ This visualizer also allows sorting of the bar plot according to the calculated visualizer.fit(X_pd, y) # Fit the data to the visualizer visualizer.show() # Finalize and render the figure + Quick Method -------------------------- +------------ + The same functionality above can be achieved with the associated quick method ``feature_correlation``. This method will build the ``FeatureCorrelation`` object with the associated arguments, fit it, then (optionally) immediately show it .. plot:: @@ -122,6 +125,7 @@ The same functionality above can be achieved with the associated quick method `` visualizer = feature_correlation(X, y, labels=features) plt.tight_layout() + API Reference ------------- diff --git a/docs/api/text/dispersion.rst b/docs/api/text/dispersion.rst index 031dbd29b..cc71940dd 100644 --- a/docs/api/text/dispersion.rst +++ b/docs/api/text/dispersion.rst @@ -6,8 +6,8 @@ Dispersion Plot A word's importance can be weighed by its dispersion in a corpus. Lexical dispersion is a measure of a word's homogeneity across the parts of a corpus. This plot notes the occurrences of a word and how many words from the beginning of the corpus it appears. ================= ============================== -Visualizer `DispersionPlot `_ -Quick Method `dispersion() `_ +Visualizer :class:`~yellowbrick.text.dispersion.DispersionPlot` +Quick Method :func:`~yellowbrick.text.dispersion.dispersion()` Models Text Modeling Workflow Feature Engineering ================= ============================== @@ -34,7 +34,8 @@ Workflow Feature Engineering visualizer.show() Quick Method -------------------------- +------------ + The same functionality above can be achieved with the associated quick method `dispersion`. This method will build the Dispersion Plot object with the associated arguments, fit it, then (optionally) immediately show the visualization. .. plot:: @@ -56,6 +57,7 @@ The same functionality above can be achieved with the associated quick method `d # Create the visualizer and draw the plot dispersion(target_words, text) + API Reference ------------- diff --git a/docs/api/text/postag.rst b/docs/api/text/postag.rst index 206df0ad9..25207db44 100644 --- a/docs/api/text/postag.rst +++ b/docs/api/text/postag.rst @@ -8,8 +8,8 @@ Parts of speech (e.g. verbs, nouns, prepositions, adjectives) indicate how a wor The ``PosTagVisualizer`` is intended to support grammar-based feature extraction techniques for machine learning workflows that require natural language processing. The visualizer can either read in a corpus that has already been sentence- and word-segmented, and tagged, or perform this tagging automatically by specifying the parser to use (nltk or spacy). The visualizer creates a bar chart to visualize the relative proportions of different parts-of-speech in a corpus. ================= ================= -Visualizer `PosTagVisualizer `_ -Quick Method `postag() `_ +Visualizer :class:`~yellowbrick.text.postag.PosTagVisualizer` +Quick Method :func:`~yellowbrick.text.postag.postag` Models Classification, Regression Workflow Feature Engineering ================= ================= @@ -92,7 +92,9 @@ Universal Dependencies Tags Libraries like SpaCy use tags from the Universal Dependencies (UD) framework. The ``PosTagVisualizer`` can also be used with text tagged using this framework by specifying the ``tagset`` keyword as "universal" on instantiation. -.. code:: python +.. plot:: + + from yellowbrick.text import PosTagVisualizer tagged_speech = [ [ @@ -153,8 +155,10 @@ Libraries like SpaCy use tags from the Universal Dependencies (UD) framework. Th viz.fit(tagged_speech) viz.show() + Quick Method -------------------------- +------------ + The same functionality above can be achieved with the associated quick method ``postag``. This method will build the ``PosTagVisualizer`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. .. plot:: @@ -184,8 +188,12 @@ The same functionality above can be achieved with the associated quick method `` # Create the visualizer, fit, score, and show it postag(machado) + plt.tight_layout() +Part of Speech Tags +------------------- + +-------------------+------------------------------------------+----------------------+--------------------------+ | Penn-Treebank Tag | Description | Universal Tag | Description | +===================+==========================================+======================+==========================+ @@ -315,6 +323,7 @@ Or using `wordpunct`. viz.fit(untagged_speech) viz.show() + API Reference ------------- diff --git a/docs/api/text/tsne.rst b/docs/api/text/tsne.rst index 16f38c7e3..77bba4a97 100644 --- a/docs/api/text/tsne.rst +++ b/docs/api/text/tsne.rst @@ -4,8 +4,8 @@ t-SNE Corpus Visualization ========================== ================= ================= -Visualizer `TSNEVisualizer `_ -Quick Method `tsne() `_ +Visualizer :class:`~yellowbrick.text.tsne.TSNEVisualizer` +Quick Method :func:`~yellowbrick.text.tsne.tsne` Models Decomposition Workflow Feature Engineering/Selection ================= ================= @@ -94,7 +94,8 @@ This means we don't have to use class labels at all. Instead we can use cluster tsne.show() Quick Method -------------------------- +------------ + The same functionality above can be achieved with the associated quick method ``tsne``. This method will build the ``TSNEVisualizer`` object with the associated arguments, fit it, then (optionally) immediately show it .. plot:: @@ -114,6 +115,7 @@ The same functionality above can be achieved with the associated quick method `` tsne(X, y) + API Reference ------------- diff --git a/docs/api/text/umap_vis.rst b/docs/api/text/umap_vis.rst index b980e6d4b..41ccadbab 100644 --- a/docs/api/text/umap_vis.rst +++ b/docs/api/text/umap_vis.rst @@ -138,13 +138,15 @@ Quick Method The same functionality above can be achieved with the associated quick method ``umap``. This method will build the ``UMAPVisualizer`` object with the associated arguments, fit it, then (optionally) immediately show it -.. code:: python +.. plot:: + :context: close-figs + :alt: Quick method umap plot of the clustered hobbies corpus from sklearn.cluster import KMeans from sklearn.feature_extraction.text import TfidfVectorizer + from yellowbrick.text import umap from yellowbrick.datasets import load_hobbies - from yellowbrick.text import UMAPVisualizer # Load the text data corpus = load_hobbies() @@ -156,7 +158,7 @@ The same functionality above can be achieved with the associated quick method `` clusters = KMeans(n_clusters=5) clusters.fit(docs) - umap(docs, ["c{}".format(c) for c in clusters.labels_]) + viz = umap(docs, ["c{}".format(c) for c in clusters.labels_]) API Reference diff --git a/yellowbrick/features/pca.py b/yellowbrick/features/pca.py index 6b09dfca5..913c134ba 100644 --- a/yellowbrick/features/pca.py +++ b/yellowbrick/features/pca.py @@ -591,6 +591,7 @@ def pca_decomposition( visualizer = PCA( ax=ax, features=features, + classes=classes, scale=scale, projection=projection, proj_features=proj_features, diff --git a/yellowbrick/features/pcoords.py b/yellowbrick/features/pcoords.py index 5cf433211..c0f0a06b8 100644 --- a/yellowbrick/features/pcoords.py +++ b/yellowbrick/features/pcoords.py @@ -34,7 +34,6 @@ ## Quick Methods ########################################################################## - def parallel_coordinates( X, y, @@ -61,7 +60,6 @@ def parallel_coordinates( Parameters ---------- - X : ndarray or DataFrame of shape n x m A matrix of n instances with m features @@ -256,7 +254,7 @@ class ParallelCoordinates(DataVisualizer): the visualization as defined in other Visualizers. Attributes - -------- + ---------- n_samples_ : int number of samples included in the visualization object @@ -278,12 +276,6 @@ class ParallelCoordinates(DataVisualizer): >>> visualizer.fit(X, y) >>> visualizer.transform(X) >>> visualizer.show() - - Notes - ----- - - These parameters can be influenced later on in the visualization - process, but can and should be set as early as possible. """ NORMALIZERS = { diff --git a/yellowbrick/text/umap_vis.py b/yellowbrick/text/umap_vis.py index 95ae8894e..276257b62 100644 --- a/yellowbrick/text/umap_vis.py +++ b/yellowbrick/text/umap_vis.py @@ -117,8 +117,8 @@ def umap( ax=ax, classes=classes, colors=colors, colormap=colormap, alpha=alpha, **kwargs ) - # Fit and transform the visualizer (calls draw) - visualizer.fit_transform(X, y, **kwargs) + # Fit the visualizer (calls draw) + visualizer.fit(X, y, **kwargs) if show: visualizer.show() From 66140e802576ede59ab493526e5a2d88bdeb0433 Mon Sep 17 00:00:00 2001 From: Rebecca Bilbro Date: Tue, 25 Feb 2020 18:36:42 -0500 Subject: [PATCH 43/44] Adds v1.1 changelog ahead of release (#1039) --- docs/changelog.rst | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/docs/changelog.rst b/docs/changelog.rst index 11c69efe9..4f3aa7ae9 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -3,11 +3,29 @@ Changelog ========= +Version 1.1 +----------- + +* Tag: v1.1_ +* Deployed Wednesday, February 12, 2020 +* Contributors: Benjamin Bengfort, Rebecca Bilbro, Kristen McIntyre, Larry Gray, Prema Roman, Adam Morris, Shivendra Sharma, Michael Chestnut, Michael Garod, Naresh Bachwani, Piyush Gautam, Daniel Navarrete, Molly Morrison, Emma Kwiecinska, Sarthak Jain, Tony Ojeda, Edwin Schmier, Nathan Danielsen + +Major Changes: + - Quick methods (aka Oneliners), which return a fully fitted finalized visualizer object in only a single line, are now implemented for all Yellowbrick Visualizers. Test coverage has been added for all quick methods. The documentation has been updated to document and demonstrate the usage of the quick methods. + - Added Part of Speech tagging for raw text using spaCy and NLTK to POSTagVisualizer. + +Minor Changes: + - Adds Board of Directors minutes for Spring meeting. + - Miscellaneous documentation corrections and fixes. + - Miscellaneous CI and testing corrections and fixes. + +.. _v1.1: https://github.com/DistrictDataLabs/yellowbrick/releases/tag/v1.1 + Hotfix 1.0.1 ------------ * Tag: v1.0.1_ -* Deployed +* Deployed Sunday, October 6, 2019 * Contributors: Benjamin Bengfort, Rebecca Bilbro, Kristen McIntyre .. warning:: **Major API change**: the ``poof()`` method is now deprecated, please use ``show()`` instead. After a significant discussion with community members we have deprecated our original "make the magic happen" method due to concerns about the usage of the word. We've renamed the original method to and created a stub method with the original name that issues a deprecation warning and calls ``show()``. @@ -28,7 +46,7 @@ Version 1.0 ----------- * Tag: v1.0_ -* Deployed +* Deployed Wednesday, August 28, 2019 * Contributors: Benjamin Bengfort, Rebecca Bilbro, Nathan Danielsen, Kristen McIntyre, Larry Gray, Prema Roman, Adam Morris, Tony Ojeda, Edwin Schmier, Carl Dawson, Daniel Navarrete, Francois Dion, Halee Mason, Jeff Hale, Jiayi Zhang, Jimmy Shah, John Healy, Justin Ormont, Kevin Arvai, Michael Garod, Mike Curry, Nabanita Dash, Naresh Bachwani, Nicholas A. Brown, Piyush Gautam, Pradeep Singh, Rohit Ganapathy, Ry Whittington, Sangarshanan, Sourav Singh, Thomas J Fan, Zijie (ZJ) Poh, Zonghan, Xie .. warning:: **Python 2 Deprecation**: Please note that this release deprecates Yellowbrick's support for Python 2.7. After careful consideration and following the lead of our primary dependencies (NumPy, scikit-learn, and Matplolib), we have chosen to move forward with the community and support Python 3.4 and later. From 4d6cb3e0e588f566792640b01cc0d8b0fc3dee08 Mon Sep 17 00:00:00 2001 From: Benjamin Bengfort Date: Tue, 25 Feb 2020 19:08:19 -0500 Subject: [PATCH 44/44] version bump to v1.1 --- docs/changelog.rst | 2 +- tests/__init__.py | 2 +- yellowbrick/version.py | 6 +++--- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/changelog.rst b/docs/changelog.rst index 4f3aa7ae9..1f9fabf1f 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -13,7 +13,7 @@ Version 1.1 Major Changes: - Quick methods (aka Oneliners), which return a fully fitted finalized visualizer object in only a single line, are now implemented for all Yellowbrick Visualizers. Test coverage has been added for all quick methods. The documentation has been updated to document and demonstrate the usage of the quick methods. - Added Part of Speech tagging for raw text using spaCy and NLTK to POSTagVisualizer. - + Minor Changes: - Adds Board of Directors minutes for Spring meeting. - Miscellaneous documentation corrections and fixes. diff --git a/tests/__init__.py b/tests/__init__.py index d15f1d4e6..533cdee65 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -27,7 +27,7 @@ ## Test Constants ########################################################################## -EXPECTED_VERSION = "1.0.1" +EXPECTED_VERSION = "1.1" ########################################################################## diff --git a/yellowbrick/version.py b/yellowbrick/version.py index 11b80ef33..95f134ddc 100644 --- a/yellowbrick/version.py +++ b/yellowbrick/version.py @@ -19,11 +19,11 @@ __version_info__ = { "major": 1, - "minor": 0, - "micro": 1, + "minor": 1, + "micro": 0, "releaselevel": "final", "post": 0, - "serial": 16, + "serial": 17, } ##########################################################################

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zFfgrKaj6i*Jf>UBi&xq?8dhpsw6nE72hhV5fQ2Q`H_%aG*3)8wlsQkF$YEFBPz8_- zVz$M1d&5gpTN{TUm7=E-EmxE7%DEA43=|lV01(QA(u1y@VMEP-HIa7d{$lfECPvsG z^)7&U^hAt21FvN7_Nsylk$~Y?f_sAi5IAu~U@#aN;5HUGm;rz&!ub0~N{j37nfc8F z0&2i})FDJ!ksGm?Lar|33!J~A(;vpPp9=JZ0I8v)yBp^2{yGCqxd@VG3Nk9StT*5{l4nhZ> zf?&7^N>Pwldjb~*j8up|JxT?@;eR5XL3gv;vHb!ha2L$jy$jOr&06m~wli0=0Ild< zke4V#9H%4b?Cq0OKMWn_28E|*=vEpdgMd6)1TYMI01IxqJj!q400d3{U9LCh{~wjR q|E}Z$Tlat6y8Q1dw*UXr=_`_ +Quick Method `manifold_embedding() `_ +Models Classification, Regression +Workflow Feature Engineering +================= ================= + .. image:: images/concrete_tsne_manifold.png The ``Manifold`` visualizer allows access to all currently available @@ -69,9 +77,10 @@ this by assigning a color to each label and showing the labels in a legend. # Load the classification dataset X, y = load_occupancy() + classes = ["unoccupied", "occupied"] # Instantiate the visualizer - viz = Manifold(manifold="tsne") + viz = Manifold(manifold="tsne", classes=classes) viz.fit_transform(X, y) # Fit the data to the visualizer viz.show() # Finalize and render the figure @@ -103,15 +112,16 @@ the ``f_classif`` score to find the 3 best features in our occupancy dataset. # Load the classification dataset X, y = load_occupancy() + classes = ["unoccupied", "occupied"] # Create a pipeline model = Pipeline([ ("selectk", SelectKBest(k=3, score_func=f_classif)), - ("viz", Manifold(manifold="isomap", n_neighbors=10)), + ("viz", Manifold(manifold="isomap", n_neighbors=10, classes=classes)), ]) model.fit_transform(X, y) # Fit the data to the model - model.named_steps['viz'].show() # Finalize and render the figure + model.named_steps['viz'].show() # Finalize and render the figure .. image:: images/occupancy_select_k_best_isomap_manifold.png @@ -142,11 +152,32 @@ continuous by counting the number of unique values in ``y``. .. image:: images/concrete_isomap_manifold.png + +Quick Method +----------------- +The same functionality above can be achieved with the associated quick method ``manifold_embedding``. This method will build the ``Manifold`` object with the associated arguments, fit it, then (optionally) immediately show the visualization. + +.. note to contributors: the below code takes a long time to run so has not been + modified with a plot directive. See manifold.py to regenerate images. + +.. code:: python + + from yellowbrick.features.Manifold import manifold_embedding + from yellowbrick.datasets import load_concrete + + # Load the regression dataset + X, y = load_concrete() + + # Instantiate the visualizer + manifold_embedding(X, y, manifold="isomap", n_neighbors=10) + +.. image:: images/manifold_quick_method.png + API Reference ------------- .. automodule:: yellowbrick.features.manifold - :members: Manifold + :members: Manifold, manifold_embedding :undoc-members: :show-inheritance: @@ -155,7 +186,7 @@ API Reference .. _`manifold comparisons`: http://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html .. _`Locally Linear Embedding`: http://scikit-learn.org/stable/modules/manifold.html#locally-linear-embedding .. _`LTSA LLE`: http://scikit-learn.org/stable/modules/manifold.html#local-tangent-space-alignment -.. _`Hessian LLE`: http://scikit-learn.org/stable/modules/manifold.html#hessian-eigenmapping> +.. _`Hessian LLE`: http://scikit-learn.org/stable/modules/manifold.html#hessian-eigenmapping .. _`Modified LLE`: http://scikit-learn.org/stable/modules/manifold.html#modified-locally-linear-embedding .. _`Isomap`: http://scikit-learn.org/stable/modules/manifold.html#isomap .. _`MDS`: http://scikit-learn.org/stable/modules/manifold.html#multi-dimensional-scaling-mds diff --git a/docs/api/text/postag.rst b/docs/api/text/postag.rst index 4de438ce7..206df0ad9 100644 --- a/docs/api/text/postag.rst +++ b/docs/api/text/postag.rst @@ -321,4 +321,4 @@ API Reference .. automodule:: yellowbrick.text.postag :members: PosTagVisualizer, postag :undoc-members: - :show-inheritance: \ No newline at end of file + :show-inheritance: diff --git a/tests/baseline_images/test_features/test_manifold/test_manifold_quick_method_continuous_target.png b/tests/baseline_images/test_features/test_manifold/test_manifold_quick_method_continuous_target.png new file mode 100644 index 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