^jɀ#dZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z ddlZddlmZmZmZmZmZddlmZdd lmZgd ZerddlZd ed ed dfdZdede ed efdZ dde e efd'e ed(e e eddde e eefde e eefde ede ede ede e eefde ed e!d efd)Z$d*ed+ed,ed-e!d ef d.Z%d*ed/ed efd0Z&d1e eefd2e ed3e e edfde ed4ed5e e ed6e e ejNefd7ed!ed efd8Z( d?de eefd9ed2e e ede ed4ed6e e ejNefd7ed!ed efd:Z) d@de eefddd9ede e eefde ed2e e ede ed4ed6e e ejNefd!ed efd;Z*y)AzPlotting library.N)deepcopy)BytesIO) TYPE_CHECKINGAnyDictListOptionalTupleUnion)Booster_data_from_pandas_is_zero _log_warning _MissingType) pd_DataFrame) LGBMModel)create_tree_digraphplot_importance plot_metricplot_split_value_histogram plot_treeobjobj_namereturnc\t|trt|dk7rt|dy)z6Check object is not tuple or does not have 2 elements.z must be a tuple of 2 elements.N) isinstancetuplelen TypeError)rrs \/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/lightgbm/plotting.py_check_not_tuple_of_2_elementsr#s. c5 !SX]8*$CDEE&3value precisioncL|t|ts|d|dSt|S)N.f)rstr)r%r&s r" _float2strr+!s1'0' "kadejakkr$boosteraxzOptional[matplotlib.axes.Axes]heightxlimylimtitlexlabelylabelimportance_typemax_num_features ignore_zerofigsizedpigridkwargsc " ddlm}t|tr|dk(r |j }|j }n#t|tr|dk(rd}n td|j|}|j}t|s tdtt||d d }| r|Dcgc] }|d dkDs |}}| | dkDr|| d}|s tdt|dd i\}}|&| t| d|j!d d | | \}}t#j$t|}|j&||fd|d|t||d D]7\}}|j)|d zt+||dk(r t-||n|d9|j/||j1|| t|dndt3|dzf}|j5|| t|dn dt|f}|j7|||j9||#|j;d|}|j=|||j?||jA| |S#t$r}td|d}~wwxYwcc}w)a Plot model's feature importances. Parameters ---------- booster : Booster or LGBMModel Booster or LGBMModel instance which feature importance should be plotted. ax : matplotlib.axes.Axes or None, optional (default=None) Target axes instance. If None, new figure and axes will be created. height : float, optional (default=0.2) Bar height, passed to ``ax.barh()``. xlim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.xlim()``. ylim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.ylim()``. title : str or None, optional (default="Feature importance") Axes title. If None, title is disabled. xlabel : str or None, optional (default="Feature importance") X-axis title label. If None, title is disabled. @importance_type@ placeholder can be used, and it will be replaced with the value of ``importance_type`` parameter. ylabel : str or None, optional (default="Features") Y-axis title label. If None, title is disabled. importance_type : str, optional (default="auto") How the importance is calculated. If "auto", if ``booster`` parameter is LGBMModel, ``booster.importance_type`` attribute is used; "split" otherwise. If "split", result contains numbers of times the feature is used in a model. If "gain", result contains total gains of splits which use the feature. max_num_features : int or None, optional (default=None) Max number of top features displayed on plot. If None or <1, all features will be displayed. ignore_zero : bool, optional (default=True) Whether to ignore features with zero importance. figsize : tuple of 2 elements or None, optional (default=None) Figure size. dpi : int or None, optional (default=None) Resolution of the figure. grid : bool, optional (default=True) Whether to add a grid for axes. precision : int or None, optional (default=3) Used to restrict the display of floating point values to a certain precision. **kwargs Other parameters passed to ``ax.barh()``. Returns ------- ax : matplotlib.axes.Axes The plot with model's feature importances. rNzHYou must install matplotlib and restart your session to plot importance.autosplit%booster must be Booster or LGBMModel.)r4z&Booster's feature_importance is empty.T)strictc |dS)Nr )xs r"z!plot_importance..s aPQdr$)keyr zpNo non-zero feature importances found. The model may have no splits. Use ignore_zero=False to show all features.r?r7r7r8center)alignr.gain)var/皙?r0z@importance_type@)!matplotlib.pyplotpyplot ImportErrorrrr4booster_r r!feature_importance feature_namer ValueErrorsortedzipr#subplotsnparangebarhtextfloatr+ set_yticksset_yticklabelsmaxset_xlimset_ylim set_titlereplace set_xlabel set_ylabelr9)r,r-r.r/r0r1r2r3r4r5r6r7r8r9r&r:plterr importancerQtuplesrBlabelsvalues_ylocsys r"rr%sJo''9% f $%55O"" GW % f $%O?@@++O+LJ'')L z?ABB C j>N SF#0qtax!00#(81(<))*+  :  &..NFF z   *7I > Q7 <2 IIc&k "E BGGE6C&CFCFE$/l1 AuQx_PV=VAy!9\]bjklMM%v &tV43v;$%KK &tV4CK KK  U  3_E f  fGGDM IC odeknno*1s#I/1 J ?J / J 8 JJ featurebins width_coefc  4 ddlm}ddlm}t |t r |j}nt |ts td|j||d\}}tj|dk(rtd|d ||d |dz z}|dd |d dzd z }|&| t| d |jd d | | \}}|j ||fd|d| | t|dn!|d |dz }|d|dzz |d |dzzf}|j#||j$j'|d| t|dndt)|dzf}|j+||P|j-dt/|}|j-dt |t.rdnd}|j1|||j3|| |j5| |j7| |S#t$r}t d|d}~wwxYw)aPlot split value histogram for the specified feature of the model. Parameters ---------- booster : Booster or LGBMModel Booster or LGBMModel instance of which feature split value histogram should be plotted. feature : int or str The feature name or index the histogram is plotted for. If int, interpreted as index. If str, interpreted as name. bins : int, str or None, optional (default=None) The maximum number of bins. If None, the number of bins equals number of unique split values. If str, it should be one from the list of the supported values by ``numpy.histogram()`` function. ax : matplotlib.axes.Axes or None, optional (default=None) Target axes instance. If None, new figure and axes will be created. width_coef : float, optional (default=0.8) Coefficient for histogram bar width. xlim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.xlim()``. ylim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.ylim()``. title : str or None, optional (default="Split value histogram for feature with @index/name@ @feature@") Axes title. If None, title is disabled. @feature@ placeholder can be used, and it will be replaced with the value of ``feature`` parameter. @index/name@ placeholder can be used, and it will be replaced with ``index`` word in case of ``int`` type ``feature`` parameter or ``name`` word in case of ``str`` type ``feature`` parameter. xlabel : str or None, optional (default="Feature split value") X-axis title label. If None, title is disabled. ylabel : str or None, optional (default="Count") Y-axis title label. If None, title is disabled. figsize : tuple of 2 elements or None, optional (default=None) Figure size. dpi : int or None, optional (default=None) Resolution of the figure. grid : bool, optional (default=True) Whether to add a grid for axes. **kwargs Other parameters passed to ``ax.bar()``. Returns ------- ax : matplotlib.axes.Axes The plot with specified model's feature split value histogram. rN) MaxNLocatorzSYou must install matplotlib and restart your session to plot split value histogram.r>F)rmrn xgboost_stylez3Cannot plot split value histogram, because feature z was not used in splittingr rKrr7rErF)rGwidthr/皙?T)integerr0rJz @feature@z @index/name@nameindex)rLrMmatplotlib.tickerrqrNrrrOr r!get_split_value_histogramrV count_nonzerorRr#rUbarr^yaxisset_major_locatorr]r_rar*r`rbrcr9)r,rmrnr-ror/r0r1r2r3r7r8r9r:rdrqrehist split_binsrscentredrj range_results r"rrs4D'1 '9%""  )?@@88tch8iD* "NwiWqrss *Q-*Q-7 8E#2AB/14G z   *7I > Q7 <2 BFF7D@@@ &tV4!"~ 1 5 1 s 22JrN\TWEW4WXKKHH{489 &tV43t9s?#KK  k3w<8 nGS9QvW^` U  f  fGGDM I]  a  s G== H HHmetric dataset_namesc r ddlm} t|trt |j }nBt|tr t |}n&t|tr tdtdt|}|s td|&| t| d| jdd| | \}}|t|j}n3t|t t"t$fr|s td t|}t'|}||}t|}|$|dkDr t)d |j+\}}n||vr t-d ||}t|}t/|}t1|}t3|}|j5||| |D]:}||}||}t/g||}t1g||}|j5||| <|j7d| t|dnd|f}|j9|| t|dn||z }||dzz ||dzzf}|j;|||j=|||j?||#|jAd|}|jC||jE| |S#t$r} td| d} ~ wwxYw)aPlot one metric during training. Parameters ---------- booster : dict or LGBMModel Dictionary returned from ``lightgbm.train()`` or LGBMModel instance. metric : str or None, optional (default=None) The metric name to plot. Only one metric supported because different metrics have various scales. If None, first metric picked from dictionary (according to hashcode). dataset_names : list of str, or None, optional (default=None) List of the dataset names which are used to calculate metric to plot. If None, all datasets are used. ax : matplotlib.axes.Axes or None, optional (default=None) Target axes instance. If None, new figure and axes will be created. xlim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.xlim()``. ylim : tuple of 2 elements or None, optional (default=None) Tuple passed to ``ax.ylim()``. title : str or None, optional (default="Metric during training") Axes title. If None, title is disabled. xlabel : str or None, optional (default="Iterations") X-axis title label. If None, title is disabled. ylabel : str or None, optional (default="@metric@") Y-axis title label. If 'auto', metric name is used. If None, title is disabled. @metric@ placeholder can be used, and it will be replaced with metric name. figsize : tuple of 2 elements or None, optional (default=None) Figure size. dpi : int or None, optional (default=None) Resolution of the figure. grid : bool, optional (default=True) Whether to add a grid for axes. Returns ------- ax : matplotlib.axes.Axes The plot with metric's history over the training. rNzDYou must install matplotlib and restart your session to plot metric.zbooster must be dict or LGBMModel. To use plot_metric with Booster type, first record the metrics using record_evaluation callback then pass that to plot_metric as argument `booster`z"booster must be dict or LGBMModel.zeval results cannot be empty.r7r rEz4dataset_names should be iterable and cannot be emptyz4More than one metric available, picking one to plot.z No given metric in eval results.)labelbest)locr/r0rt@metric@)#rLrMrNrrr evals_result_dictr r!r rRr#rUiterkeyslistrsetnextrpopitemKeyErrorr]minrangeplotlegendr^r_r`rbrarcr9)r,rrr-r/r0r1r2r3r7r8r9rdre eval_resultsnum_datarjdataset_names_iterrvmetrics_for_one num_metricresults num_iteration max_result min_resultx_rs r"rr&srk''9% 5 56 GT "( GW % E  <==< H 899 z   *7I > Q7 <2!,"3"3"56  eS'9 :-OPP!-0 " #D"4(O_%J ~ > O P)113  (=> >!&)LMWJWJ } BGGBtG$")&t,!&).'.:. .'.:.  G4( )II&I &tV4=!KK &tV4!J. \C//lS>P1PQKK  U  f  F3 fGGDM I] k`agjjksJ J6% J11J6fval thresholdmissing_type_str default_leftc"t|}tj|r|tjk7rd}|tjk(r t |s(|tjk(rtj|r |rd}|Sd}|S||krdnd}|S)Ngleftright)rmathisnanNANZEROr)rrrr missing_type directions r"&_determine_direction_for_numeric_splitrs 01L zz$LL,<,<< )))htn (((TZZ-=*F  18  #i/FW r$ thresholdsctj|st|dkry|jdDchc] }t|}}t||vrdSdScc}w)Nrr||r)rrintr=)rrtint_thresholdss r"*_determine_direction_for_categorical_splitrsX zz$3t9q=&0&6&6t&<=c!f=N=Y.06=g=>sA tree_info show_info feature_names orientation constraints example_casemax_category_valuesc   ddlm} dttt fdt dttdttd td df fd | di||d k(rd nd} jddd| d|dvr |d|dddddun tdrd} jd| ddS#t$r} td| d} ~ wwxYw)z~Convert specified tree to graphviz instance. See: - https://graphviz.readthedocs.io/en/stable/api.html#digraph r)Digraphz@You must install graphviz and restart your session to plot tree.Nroot total_countparentdecision highlightrc d}d}d}|rd}d} nd}d} d|vrd } d } d } |d } |d dk(rd}n|d dk(rd}n tdd|d}|d} d|d|}n d|d|d}d}7|d dk(rt||d }nt||d |d|d}|d dk(rD|d jd}t |kDr"|d }dj |dddz|d z} |dt | d!z }d"D]n}|vs|jd#d }|d$vr|d%t ||d|z }9|d&k(r|d%|d'||z }M|d(k(sS|d%t |d&|z d)zdd*z }pr|dd+k(rd,}|dd k(rd-}d.}d/|d0}|d1||| |xr|d2k(3|d4||| |xr|d5k(3nxd6} d7|d8}d9|d8d'}|dt |d:d!z }d;vr|d%t |d;d<z }d=vr |d>|d=z }d(vr|d%t |d=|z d)zdd*z }d/|d0}j||| |||| |?|j||||| @yy)AzRecursively add node or edge.whiteNblue3black1 split_index rectangleyesnor decision_typez<=z≤z===z$Invalid decision type in tree model.r= split_featurezz z feature  )rrrr)rrrrrrz||...||rKz) split_gaininternal_valueinternal_weightinternal_countdata_percentagerj>rrrz
rz: rdz % of datar z#ddffddz#ffddddfilled<> left_childrrrrrr right_childrellipseleaf leaf_indexzleaf leaf_value leaf_weightz weight leaf_countz
count: )rshapestyle fillcolorcolorpenwidthtooltip)rr) rRrrr=r joinr+nodeedge)rrrrrrrrrrrl_decr_decroperatorrvrrrcategory_valuesinfooutputaddrrrgraphrr&rs r"rz_to_graphviz..adds   EHEH D EEE[)IO$,$o&$. !GHH4 ./0D 1M(mM:;5 K%m_E(1EI'(D0 J)-8T+EV!I!G)-8"&{"3)-n)=%).%9 !I O$,"&{"3"9"9$"?'*==";/G $ /"1*= > J_]_M` `I s:i;,Ls,RTU!V WW`aawaLE      JJvtXUXJ N r$ horizontalLRTBrz0.05z0.3)nodeseprankseprankdirrtree_structurerzCannot plot trees with no splita<
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>rrr)rrrrA) graphvizrrNrr*rrr boolattr Exceptionr)rrrr&rrrrr:rrerrrrs ``` ``` @@r" _to_graphvizrs-"g$hO38nhO+.hO8@ hOQYZ]Q^hOkohO hOhOT  f E!\1dtG JJwwJG9%566 +,!"234DE"$.  9::  86G L L[ g\]cffgsC C CC tree_indexc ft|tr |j}nt|ts t d|j }|d} |j dd} |j dd} |t| kr| |} n td|g}|t|tjtfr|jdk7r td|jd d k7r td t|trt|d d |j d }|d }t#d| || ||| ||d|S)a Create a digraph representation of specified tree. Each node in the graph represents a node in the tree. Non-leaf nodes have labels like ``Column_10 <= 875.9``, which means "this node splits on the feature named "Column_10", with threshold 875.9". Leaf nodes have labels like ``leaf 2: 0.422``, which means "this node is a leaf node, and the predicted value for records that fall into this node is 0.422". The number (``2``) is an internal unique identifier and doesn't have any special meaning. .. note:: For more information please visit https://graphviz.readthedocs.io/en/stable/api.html#digraph. Parameters ---------- booster : Booster or LGBMModel Booster or LGBMModel instance to be converted. tree_index : int, optional (default=0) The index of a target tree to convert. show_info : list of str, or None, optional (default=None) What information should be shown in nodes. - ``'split_gain'`` : gain from adding this split to the model - ``'internal_value'`` : raw predicted value that would be produced by this node if it was a leaf node - ``'internal_count'`` : number of records from the training data that fall into this non-leaf node - ``'internal_weight'`` : total weight of all nodes that fall into this non-leaf node - ``'leaf_count'`` : number of records from the training data that fall into this leaf node - ``'leaf_weight'`` : total weight (sum of Hessian) of all observations that fall into this leaf node - ``'data_percentage'`` : percentage of training data that fall into this node precision : int or None, optional (default=3) Used to restrict the display of floating point values to a certain precision. orientation : str, optional (default='horizontal') Orientation of the tree. Can be 'horizontal' or 'vertical'. example_case : numpy 2-D array, pandas DataFrame or None, optional (default=None) Single row with the same structure as the training data. If not None, the plot will highlight the path that sample takes through the tree. .. versionadded:: 4.0.0 max_category_values : int, optional (default=10) The maximum number of category values to display in tree nodes, if the number of thresholds is greater than this value, thresholds will be collapsed and displayed on the label tooltip instead. .. warning:: Consider wrapping the SVG string of the tree graph with ``IPython.display.HTML`` when running on JupyterLab to get the `tooltip `_ working right. Example: .. code-block:: python from IPython.display import HTML graph = lgb.create_tree_digraph(clf, max_category_values=5) HTML(graph._repr_image_svg_xml()) .. versionadded:: 4.0.0 **kwargs Other parameters passed to ``Digraph`` constructor. Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters. Returns ------- graph : graphviz.Digraph The digraph representation of specified tree. r>rrNmonotone_constraintsztree_index is out of range.rz Q7 <2  !    E  AGGEJJeJ $%FF1I    ! !! $CIIcNGGEN I3 i^_ehhis C C CC)NrtNNFeature importancerFeaturesr<NTNNT) NNg?NNz=Split value histogram for feature with @index/name@ @feature@zFeature split valueCountNNT) NNNNNzMetric during training IterationsrNNT)rNrrN )NrNNNrrN)+__doc__rcopyriortypingrrrrr r r numpyrVbasicr rrrrcompatrsklearnr__all__rr*r#rZrr+rrrrrrr rrrrAr$r"r,sF IIISS  FFsFtF lel l#l ,0*.*./0&!&*-1 H 7I% &H(H H 5& ' H 5& ' H C= H SMH SMHHsmHHeE5L) *H #H H}H !H" #H\#'+/*.*.Z1#-1s 7I% &s 38_s S$ s ) s  s 5& ' s 5& 's C=s SMs SMseE5L) *s #s ss sp!)-+/*.*.3(&-1I 4? #I SMIDI&I ) I 5& ' I 5& ' I C=I SMI SMIeE5L) *I #I I IX      &>U>>PS>`CH~`Cy`cD) ` } `  `$s)$`5\!9:;``` `J%) #>B!{ 7I% &{{S "{} {  { 5\!9:; {{{ {@,0-1%) #>Bd 7I% &d(ddeE5L) * d # d S " d}dd5\!9:;dd dr$