github.com/metaprov/modela-operator@v0.0.0-20240118193048-f378be8b74d2/manifests/modela-catalog/algorithms/regressors.yaml (about)

     1  apiVersion: catalog.modela.ai/v1alpha1
     2  kind: Algorithm
     3  metadata:
     4    labels:
     5      app.kubernetes.io/part-of: modela
     6    name: ada-boost-regressor
     7    namespace: modela-catalog
     8  spec:
     9    frameworkName: scikit-learn
    10    ranges:
    11    - high: 500
    12      low: 50
    13      name: ada_n_estimators
    14      type: int
    15    - high: 1.0
    16      log: true
    17      low: 0.01
    18      name: ada_learning_rate
    19      type: float
    20    tasks:
    21    - regression
    22  
    23  ---
    24  apiVersion: catalog.modela.ai/v1alpha1
    25  kind: Algorithm
    26  metadata:
    27    labels:
    28      app.kubernetes.io/part-of: modela
    29    name: decision-tree-regressor
    30    namespace: modela-catalog
    31  spec:
    32    frameworkName: scikit-learn
    33    ranges:
    34    - choices:
    35      - mse
    36      - mae
    37      name: dt_criterion
    38      type: categorical
    39    - choices:
    40      - best
    41      - random
    42      name: dt_splitter
    43      type: categorical
    44    - high: 20
    45      low: 2
    46      name: dt_min_samples_split
    47      type: int
    48    - high: 20
    49      low: 1
    50      name: dt_min_samples_leaf
    51      type: int
    52    tasks:
    53    - regression
    54  
    55  ---
    56  apiVersion: catalog.modela.ai/v1alpha1
    57  kind: Algorithm
    58  metadata:
    59    labels:
    60      app.kubernetes.io/part-of: modela
    61    name: elasticnet-regressor
    62    namespace: modela-catalog
    63  spec:
    64    frameworkName: scikit-learn
    65    ranges:
    66    - high: 100.0
    67      log: true
    68      low: 1.0e-05
    69      name: en_alpha
    70      type: float
    71    - high: 1
    72      log: true
    73      low: 0.01
    74      name: en_l1_ratio
    75      type: float
    76    tasks:
    77    - regression
    78  
    79  ---
    80  apiVersion: catalog.modela.ai/v1alpha1
    81  kind: Algorithm
    82  metadata:
    83    labels:
    84      app.kubernetes.io/part-of: modela
    85    name: extra-tree-regressor
    86    namespace: modela-catalog
    87  spec:
    88    frameworkName: scikit-learn
    89    ranges:
    90    - high: 1.0
    91      log: true
    92      low: 0.1
    93      name: et_max_features
    94      type: float
    95    - high: 20
    96      low: 2
    97      name: et_min_samples_split
    98      type: int
    99    - high: 20
   100      low: 1
   101      name: et_min_samples_leaf
   102      type: int
   103    tasks:
   104    - regression
   105  
   106  ---
   107  apiVersion: catalog.modela.ai/v1alpha1
   108  kind: Algorithm
   109  metadata:
   110    labels:
   111      app.kubernetes.io/part-of: modela
   112    name: gradient-boosting-regressor
   113    namespace: modela-catalog
   114  spec:
   115    frameworkName: scikit-learn
   116    ranges:
   117    - high: 0.2
   118      log: true
   119      low: 0.001
   120      name: gb_learning_rate
   121      type: float
   122    - high: 200
   123      low: 100
   124      name: gb_n_estimators
   125      type: int
   126    - high: 7
   127      low: 2
   128      name: gb_max_leaf_nodes
   129      type: int
   130    - high: 9
   131      low: 1
   132      name: gb_min_samples_leaf
   133      type: int
   134    - high: 10
   135      low: 1
   136      name: gb_max_depth
   137      type: int
   138    - high: 10
   139      low: 2
   140      name: gb_min_samples_split
   141      type: int
   142    tasks:
   143    - regression
   144  
   145  ---
   146  apiVersion: catalog.modela.ai/v1alpha1
   147  kind: Algorithm
   148  metadata:
   149    labels:
   150      app.kubernetes.io/part-of: modela
   151    name: hist-regressor
   152    namespace: modela-catalog
   153  spec:
   154    frameworkName: scikit-learn
   155    ranges:
   156    - high: 0.2
   157      log: true
   158      low: 0.001
   159      name: hist_learning_rate
   160      type: float
   161    - high: 2000
   162      low: 100
   163      name: hist_max_iter
   164      type: int
   165    - high: 7
   166      low: 2
   167      name: hist_max_leaf_nodes
   168      type: int
   169    - high: 9
   170      low: 1
   171      name: hist_min_samples_leaf
   172      type: int
   173    tasks:
   174    - regression
   175  
   176  ---
   177  apiVersion: catalog.modela.ai/v1alpha1
   178  kind: Algorithm
   179  metadata:
   180    labels:
   181      app.kubernetes.io/part-of: modela
   182    name: huber-regressor
   183    namespace: modela-catalog
   184  spec:
   185    frameworkName: scikit-learn
   186    ranges: []
   187    tasks:
   188    - regression
   189  
   190  ---
   191  apiVersion: catalog.modela.ai/v1alpha1
   192  kind: Algorithm
   193  metadata:
   194    labels:
   195      app.kubernetes.io/part-of: modela
   196    name: knn-regressor
   197    namespace: modela-catalog
   198  spec:
   199    frameworkName: scikit-learn
   200    ranges:
   201    - high: 20
   202      low: 1
   203      name: knn_n_neighbors
   204      type: int
   205    - choices:
   206      - uniform
   207      - distance
   208      name: knn_weights
   209      type: categorical
   210    tasks:
   211    - regression
   212  
   213  ---
   214  apiVersion: catalog.modela.ai/v1alpha1
   215  kind: Algorithm
   216  metadata:
   217    labels:
   218      app.kubernetes.io/part-of: modela
   219    name: lasso-regressor
   220    namespace: modela-catalog
   221  spec:
   222    frameworkName: scikit-learn
   223    ranges:
   224    - choices:
   225      - cyclic
   226      - random
   227      name: lasso_selection
   228      type: categorical
   229    tasks:
   230    - regression
   231  
   232  ---
   233  apiVersion: catalog.modela.ai/v1alpha1
   234  kind: Algorithm
   235  metadata:
   236    labels:
   237      app.kubernetes.io/part-of: modela
   238    name: lightgbm-regressor
   239    namespace: modela-catalog
   240  spec:
   241    frameworkName: scikit-learn
   242    ranges:
   243    - choices:
   244      - gbdt
   245      - dart
   246      name: lgbm_boosting_type
   247      type: categorical
   248    - high: 10
   249      low: 1
   250      name: lgbm_max_depth
   251      type: int
   252    - high: 1.0
   253      log: true
   254      low: 0.02
   255      name: lgbm_learning_rate
   256      type: float
   257    - high: 1000
   258      low: 50
   259      name: lgbm_n_estimators
   260      type: int
   261    - high: 0.01
   262      low: 0.001
   263      name: lgbm_min_child_weight
   264      type: float
   265    - high: 30
   266      low: 5
   267      name: lgbm_min_child_samples
   268      type: int
   269    - high: 1
   270      log: true
   271      low: 0.01
   272      name: lgbm_subsample
   273      type: float
   274    - high: 5
   275      low: 0
   276      name: lgbm_subsample_freq
   277      type: int
   278    - high: 1.0
   279      log: true
   280      low: 0.01
   281      name: lgbm_colsample_bytree
   282      type: float
   283    - high: 1.0
   284      log: true
   285      low: 0.0
   286      name: lgbm_reg_alpha
   287      type: float
   288    - high: 1.0
   289      log: true
   290      low: 0.0
   291      name: lgbm_reg_lambda
   292      type: float
   293    tasks:
   294    - regression
   295  
   296  ---
   297  apiVersion: catalog.modela.ai/v1alpha1
   298  kind: Algorithm
   299  metadata:
   300    labels:
   301      app.kubernetes.io/part-of: modela
   302    name: linear-regression
   303    namespace: modela-catalog
   304  spec:
   305    frameworkName: scikit-learn
   306    ranges:
   307    - choices:
   308      - 'True'
   309      - 'False'
   310      name: lreg_fit_intercept
   311      type: categorical
   312    - choices:
   313      - 'True'
   314      - 'False'
   315      name: lreg_copy_X
   316      type: categorical
   317    tasks:
   318    - regression
   319  
   320  ---
   321  apiVersion: catalog.modela.ai/v1alpha1
   322  kind: Algorithm
   323  metadata:
   324    labels:
   325      app.kubernetes.io/part-of: modela
   326    name: linear-svr
   327    namespace: modela-catalog
   328  spec:
   329    frameworkName: scikit-learn
   330    ranges:
   331    - high: 32768
   332      log: true
   333      low: 0.03125
   334      name: svr_C
   335      type: float
   336    - high: 5
   337      low: 2
   338      name: svr_degree
   339      type: int
   340    - high: 8
   341      log: true
   342      low: 3.0517578125e-05
   343      name: svr_gamma
   344      type: float
   345    - high: 1
   346      log: true
   347      low: -1
   348      name: svr_coef0
   349      type: float
   350    tasks:
   351    - regression
   352  
   353  ---
   354  apiVersion: catalog.modela.ai/v1alpha1
   355  kind: Algorithm
   356  metadata:
   357    labels:
   358      app.kubernetes.io/part-of: modela
   359    name: passive-aggressive-regressor
   360    namespace: modela-catalog
   361  spec:
   362    frameworkName: scikit-learn
   363    ranges:
   364    - high: 10
   365      log: true
   366      low: 0.03125
   367      name: pa_C
   368      type: float
   369    - choices:
   370      - epsilon_insensitive
   371      - squared_epsilon_insensitive
   372      name: pa_loss
   373      type: categorical
   374    - choices:
   375      - 'False'
   376      - 'True'
   377      name: pa_average
   378      type: categorical
   379    - high: 0.1
   380      log: true
   381      low: 1.0e-05
   382      name: pa_tol
   383      type: float
   384    tasks:
   385    - regression
   386  
   387  ---
   388  apiVersion: catalog.modela.ai/v1alpha1
   389  kind: Algorithm
   390  metadata:
   391    labels:
   392      app.kubernetes.io/part-of: modela
   393    name: runsac-regressor
   394    namespace: modela-catalog
   395  spec:
   396    frameworkName: scikit-learn
   397    ranges: []
   398    tasks:
   399    - regression
   400  
   401  ---
   402  apiVersion: catalog.modela.ai/v1alpha1
   403  kind: Algorithm
   404  metadata:
   405    labels:
   406      app.kubernetes.io/part-of: modela
   407    name: random-forest-regressor
   408    namespace: modela-catalog
   409  spec:
   410    frameworkName: scikit-learn
   411    ranges:
   412    - high: 2000
   413      low: 200
   414      name: rf_n_estimators
   415      type: int
   416    - high: 20
   417      low: 2
   418      name: rf_min_samples_split
   419      type: int
   420    - high: 1.0
   421      log: true
   422      low: 0.1
   423      name: rf_max_features
   424      type: float
   425    - high: 20
   426      low: 1
   427      name: rf_min_samples_leaf
   428      type: int
   429    tasks:
   430    - regression
   431  
   432  ---
   433  apiVersion: catalog.modela.ai/v1alpha1
   434  kind: Algorithm
   435  metadata:
   436    labels:
   437      app.kubernetes.io/part-of: modela
   438    name: ridge-regressor
   439    namespace: modela-catalog
   440  spec:
   441    frameworkName: scikit-learn
   442    ranges:
   443    - high: 10.0
   444      log: true
   445      low: 1.0e-05
   446      name: ridge_alpha
   447      type: float
   448    - high: 0.1
   449      log: true
   450      low: 1.0e-05
   451      name: ridge_tol
   452      type: float
   453    tasks:
   454    - regression
   455  
   456  ---
   457  apiVersion: catalog.modela.ai/v1alpha1
   458  kind: Algorithm
   459  metadata:
   460    labels:
   461      app.kubernetes.io/part-of: modela
   462    name: sgd-regressor
   463    namespace: modela-catalog
   464  spec:
   465    frameworkName: scikit-learn
   466    ranges:
   467    - high: 0.1
   468      log: true
   469      low: 1.0e-07
   470      name: sgd_alpha
   471      type: float
   472    - choices:
   473      - squared_loss
   474      - huber
   475      - epsilon_insensitive
   476      - squared_epsilon_insensitive
   477      name: sgd_loss
   478      type: categorical
   479    - choices:
   480      - l1
   481      - l2
   482      - elasticnet
   483      name: sgd_penalty
   484      type: categorical
   485    - choices:
   486      - optimal
   487      - invscaling
   488      - constant
   489      name: sgd_learning_rate
   490      type: categorical
   491    - high: 1
   492      log: true
   493      low: 1.0e-09
   494      name: sgd_l1_ratio
   495      type: float
   496    - high: 0.1
   497      log: true
   498      low: 1.0e-07
   499      name: sgd_eta0
   500      type: float
   501    - high: 1
   502      log: true
   503      low: 1.0e-05
   504      name: sgd_power_t
   505      type: float
   506    tasks:
   507    - regression
   508  
   509  ---
   510  apiVersion: catalog.modela.ai/v1alpha1
   511  kind: Algorithm
   512  metadata:
   513    labels:
   514      app.kubernetes.io/part-of: modela
   515    name: tailsen-regressor
   516    namespace: modela-catalog
   517  spec:
   518    frameworkName: scikit-learn
   519    ranges: []
   520    tasks:
   521    - regression
   522  
   523  ---
   524  apiVersion: catalog.modela.ai/v1alpha1
   525  kind: Algorithm
   526  metadata:
   527    labels:
   528      app.kubernetes.io/part-of: modela
   529    name: xgb-regressor
   530    namespace: modela-catalog
   531  spec:
   532    frameworkName: xgboost
   533    ranges:
   534    - high: 1
   535      log: true
   536      low: 0
   537      name: xgb_learning_rate
   538      type: float
   539    - high: 10
   540      low: 1
   541      name: xgb_max_depth
   542      type: int
   543    - high: 1
   544      log: true
   545      low: 0.5
   546      name: xgb_subsample
   547      type: float
   548    - high: 0.8
   549      low: 0.3
   550      name: xgb_colsample_bytree
   551      type: float
   552    - high: 500
   553      low: 50
   554      name: xgb_n_estimators
   555      type: int
   556    - high: 0.1
   557      log: true
   558      low: 1.0e-07
   559      name: xgb_alpha
   560      type: float
   561    tasks:
   562    - regression