U
    9%e?D                     @   s  d Z ddlmZ ddlmZmZ ddlmZ ddlZ	ddl
Z
ddlmZ ddlmZ ddlmZ dd	lmZ dd
lmZmZmZmZmZ ddgddgddgddgddgddggZddddddgZddgddgddgddgddgddggZddddddgZddddddgZdd Ze
j de!e	j"gdd Z#dd Z$dd Z%dd Z&dd Z'dd Z(e
j de!e	j"gd d! Z)d"d# Z*d$d% Z+d&d' Z,dS )(zG
Testing for export functions of decision trees (sklearn.tree.export).
    )StringIO)finditersearch)dedentN)RandomState)is_classifier)GradientBoostingClassifier)NotFittedError)DecisionTreeClassifierDecisionTreeRegressorexport_graphvizexport_text	plot_tree         g      ?c               
   C   sR  t ddddd} | tt t| d d}d}||ks8tt| dddddd d	d
}d}||ks`tt| ddd d}d}||kstt| ddd dd}d}||kstt ddddd} | jtttd} t| ddd d}d}||ksttddddd} | tt t| ddd ddd	d}d}||ks&tt dd} | tt	 t| dd d}d}d S )Nr   r   gini	max_depthmin_samples_split	criterionrandom_stateout_filea  digraph Tree {
node [shape=box, fontname="helvetica"] ;
edge [fontname="helvetica"] ;
0 [label="x[0] <= 0.0\ngini = 0.5\nsamples = 6\nvalue = [3, 3]"] ;
1 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="gini = 0.0\nsamples = 3\nvalue = [0, 3]"] ;
0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
}TFsans)filledimpurity
proportionZspecial_charactersroundedr   fontnamea  digraph Tree {
node [shape=box, style="filled, rounded", color="black", fontname="sans"] ;
edge [fontname="sans"] ;
0 [label=<x<SUB>0</SUB> &le; 0.0<br/>samples = 100.0%<br/>value = [0.5, 0.5]>, fillcolor="#ffffff"] ;
1 [label=<samples = 50.0%<br/>value = [1.0, 0.0]>, fillcolor="#e58139"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label=<samples = 50.0%<br/>value = [0.0, 1.0]>, fillcolor="#399de5"] ;
0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
}r   )r   class_namesr   zdigraph Tree {
node [shape=box, fontname="helvetica"] ;
edge [fontname="helvetica"] ;
0 [label="x[0] <= 0.0\ngini = 0.5\nsamples = 6\nvalue = [3, 3]\nclass = y[0]"] ;
1 [label="(...)"] ;
0 -> 1 ;
2 [label="(...)"] ;
0 -> 2 ;
})r   r   r   Znode_idsa;  digraph Tree {
node [shape=box, style="filled", color="black", fontname="helvetica"] ;
edge [fontname="helvetica"] ;
0 [label="node #0\nx[0] <= 0.0\ngini = 0.5\nsamples = 6\nvalue = [3, 3]", fillcolor="#ffffff"] ;
1 [label="(...)", fillcolor="#C0C0C0"] ;
0 -> 1 ;
2 [label="(...)", fillcolor="#C0C0C0"] ;
0 -> 2 ;
})Zsample_weight)r   r   r   a  digraph Tree {
node [shape=box, style="filled", color="black", fontname="helvetica"] ;
edge [fontname="helvetica"] ;
0 [label="x[0] <= 0.0\nsamples = 6\nvalue = [[3.0, 1.5, 0.0]\n[3.0, 1.0, 0.5]]", fillcolor="#ffffff"] ;
1 [label="samples = 3\nvalue = [[3, 0, 0]\n[3, 0, 0]]", fillcolor="#e58139"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="x[0] <= 1.5\nsamples = 3\nvalue = [[0.0, 1.5, 0.0]\n[0.0, 1.0, 0.5]]", fillcolor="#f1bd97"] ;
0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
3 [label="samples = 2\nvalue = [[0, 1, 0]\n[0, 1, 0]]", fillcolor="#e58139"] ;
2 -> 3 ;
4 [label="samples = 1\nvalue = [[0.0, 0.5, 0.0]\n[0.0, 0.0, 0.5]]", fillcolor="#e58139"] ;
2 -> 4 ;
}Zsquared_error)r   Zleaves_parallelr   rotater    r!   aT  digraph Tree {
node [shape=box, style="filled, rounded", color="black", fontname="sans"] ;
graph [ranksep=equally, splines=polyline] ;
edge [fontname="sans"] ;
rankdir=LR ;
0 [label="x[0] <= 0.0\nsquared_error = 1.0\nsamples = 6\nvalue = 0.0", fillcolor="#f2c09c"] ;
1 [label="squared_error = 0.0\nsamples = 3\nvalue = -1.0", fillcolor="#ffffff"] ;
0 -> 1 [labeldistance=2.5, labelangle=-45, headlabel="True"] ;
2 [label="squared_error = 0.0\nsamples = 3\nvalue = 1.0", fillcolor="#e58139"] ;
0 -> 2 [labeldistance=2.5, labelangle=45, headlabel="False"] ;
{rank=same ; 0} ;
{rank=same ; 1; 2} ;
}r   )r   r   zdigraph Tree {
node [shape=box, style="filled", color="black", fontname="helvetica"] ;
edge [fontname="helvetica"] ;
0 [label="gini = 0.0\nsamples = 6\nvalue = 6.0", fillcolor="#ffffff"] ;
})
r
   fitXyr   AssertionErrory2wr   
y_degraded)clf	contents1	contents2 r/   ]/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/sklearn/tree/tests/test_export.pytest_graphviz_toy   s                 

r1   constructorc                 C   sl   t ddddd}|tt t|| ddgd d}d}||ksBtt|| d	d
gd d}d}||kshtd S )Nr   r   r   r   Zfeature0Zfeature1)feature_namesr   a  digraph Tree {
node [shape=box, fontname="helvetica"] ;
edge [fontname="helvetica"] ;
0 [label="feature0 <= 0.0\ngini = 0.5\nsamples = 6\nvalue = [3, 3]"] ;
1 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="gini = 0.0\nsamples = 3\nvalue = [0, 3]"] ;
0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
}yesno)r"   r   a  digraph Tree {
node [shape=box, fontname="helvetica"] ;
edge [fontname="helvetica"] ;
0 [label="x[0] <= 0.0\ngini = 0.5\nsamples = 6\nvalue = [3, 3]\nclass = yes"] ;
1 [label="gini = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = yes"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="gini = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = no"] ;
0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
})r
   r%   r&   r'   r   r(   )r2   r,   r-   r.   r/   r/   r0   /test_graphviz_feature_class_names_array_support   s.        
  
 r6   c               	   C   s   t ddd} t }tt t| | W 5 Q R X | tt d}tjt	|d t| d dgd W 5 Q R X d}tjt	|d t| d dd	d
gd W 5 Q R X d}tjt
|d t| ttj W 5 Q R X t }tt t| |g d W 5 Q R X d S )Nr   r   )r   r   z?Length of feature_names, 1 does not match number of features, 2matchar3   z?Length of feature_names, 3 does not match number of features, 2bczis not an estimator instancer"   )r
   r   pytestraisesr	   r   r%   r&   r'   
ValueError	TypeErrorZtree_
IndexError)r,   outmessager/   r/   r0   test_graphviz_errors  s"    rE   c                  C   s   t ddd} | tt t }t| |d tddd} | tt | jD ]}t|d |d qHtd|	 D ]}d|
 ksltqld S )Nfriedman_mser   )r   r   r   r   )Zn_estimatorsr   z\[.*?samples.*?\])r   r%   r&   r'   r   r   r   Zestimators_r   getvaluegroupr(   )r,   dot_dataZ	estimatorfindingr/   r/   r0   test_friedman_mse_in_graphviz.  s    
rK   c            	      C   s8  t d} t d}t| d|df| d|jdddftdd	d
dtd
d	dfD ]\}}}||| dD ]}t|d |dd}td|D ]&}t	t
d|  |d
 kstqt|rd}nd}t||D ]&}t	t
d|  |d
 kstqtd|D ]*}t	t
d|  |d
 kstqqpqVd S )Nr      )   r   )     )rM   )rN   )sizerF   r   r   )r   r   r   r   r   )rO   r   T)r   	precisionr   zvalue = \d+\.\d+z\.\d+zgini = \d+\.\d+zfriedman_mse = \d+\.\d+z<= \d+\.\d+)r   zipZrandom_samplerandintr   r
   r%   r   r   lenr   rH   r(   r   )	Zrng_regZrng_clfr&   r'   r,   rR   rI   rJ   patternr/   r/   r0   test_precision=  s<      

   $$rW   c               	   C   st   t ddd} | tt d}tjt|d t| dgd W 5 Q R X d}tjt|d t| dgd	 W 5 Q R X d S )
Nr   r   rQ   z,feature_names must contain 2 elements, got 1r7   r9   r:   zWhen `class_names` is an array, it should contain as many items as `decision_tree.classes_`. Got 1 while the tree was fitted with 2 classes.r=   )r
   r%   r&   r'   r>   r?   r@   r   )r,   err_msgr/   r/   r0   test_export_text_errorsg  s    rY   c                  C   s2  t ddd} | tt td }t| |ks4tt| dd|ksHtt| dd|ks\ttd }t| dd	|ks|ttd
 }t| dd|kstddgddgddgddgddgddgddgg}dddddddg}t ddd} | || td }t| dd|kstddgddgddgddgddgddgg}ddgddgddgddgddgddgg}tddd}||| td }t|dd|kstt|ddd|kstdgdgdgdgdgdgg}tddd}||| td }t|ddgd|kstt|dddgd|ks.td S )Nr   r   rQ   zh
    |--- feature_1 <= 0.00
    |   |--- class: -1
    |--- feature_1 >  0.00
    |   |--- class: 1
    r$   
   z
    |--- feature_1 <= 0.00
    |   |--- weights: [3.00, 0.00] class: -1
    |--- feature_1 >  0.00
    |   |--- weights: [0.00, 3.00] class: 1
    T)show_weightsz\
    |- feature_1 <= 0.00
    | |- class: -1
    |- feature_1 >  0.00
    | |- class: 1
    r   )spacingr   r   rO   z{
    |--- feature_1 <= 0.00
    |   |--- class: -1
    |--- feature_1 >  0.00
    |   |--- truncated branch of depth 2
    zy
    |--- feature_1 <= 0.0
    |   |--- value: [-1.0, -1.0]
    |--- feature_1 >  0.0
    |   |--- value: [1.0, 1.0]
    )decimals)r]   r[   zq
    |--- first <= 0.0
    |   |--- value: [-1.0, -1.0]
    |--- first >  0.0
    |   |--- value: [1.0, 1.0]
    first)r]   r3   )r]   r[   r3   )	r
   r%   r&   r'   r   lstripr   r(   r   )r,   expected_reportZX_lZy_lZX_moZy_moregZX_singler/   r/   r0   test_export_textv  s@    .((rb   c                 C   sl   t ddd}|tt td }t|| ddgd|ks@ttd }t|| d	d
gd|kshtd S )Nr   r   rQ   zX
    |--- b <= 0.00
    |   |--- class: -1
    |--- b >  0.00
    |   |--- class: 1
    r9   r;   r:   zk
    |--- feature_1 <= 0.00
    |   |--- class: cat
    |--- feature_1 >  0.00
    |   |--- class: dog
    catdogr=   )r
   r%   r&   r'   r   r_   r   r(   )r2   r,   r`   r/   r/   r0   2test_export_text_feature_class_names_array_support  s    re   c                 C   s   t ddddd}|tt ddg}t||d}t|dks@t|d  d	ksTt|d
  dksht|d  dks|td S )Nr   r   Zentropyr   
first featsepal_widthr:   r   z:first feat <= 0.0
entropy = 1.0
samples = 6
value = [3, 3]r   z(entropy = 0.0
samples = 3
value = [3, 0]z(entropy = 0.0
samples = 3
value = [0, 3]r
   r%   r&   r'   r   rU   r(   Zget_textpyplotr,   r3   Znodesr/   r/   r0   test_plot_tree_entropy  s        
rk   c                 C   s   t ddddd}|tt ddg}t||d}t|dks@t|d  d	ksTt|d
  dksht|d  dks|td S )Nr   r   r   r   rf   rg   r:   r   z7first feat <= 0.0
gini = 0.5
samples = 6
value = [3, 3]r   z%gini = 0.0
samples = 3
value = [3, 0]z%gini = 0.0
samples = 3
value = [0, 3]rh   ri   r/   r/   r0   test_plot_tree_gini  s        
rl   c              	   C   s(   t  }tt t| W 5 Q R X d S )N)r   r>   r?   r	   r   )rj   r,   r/   r/   r0   test_not_fitted_tree  s    rm   )-__doc__ior   rer   r   textwrapr   numpynpr>   Znumpy.randomr   Zsklearn.baser   Zsklearn.ensembler   Zsklearn.exceptionsr	   Zsklearn.treer
   r   r   r   r   r&   r'   r)   r*   r+   r1   markZparametrizelistarrayr6   rE   rK   rW   rY   rb   re   rk   rl   rm   r/   r/   r/   r0   <module>   s:   	(( :
5 *M
