Add function parameters with their defaults to all Python methods

We want something like this

{code} Usage:

 h2o.deeplearning(x, y, training_frame, model_id = "",
   overwrite_with_best_model, validation_frame = NULL, checkpoint = NULL,
   autoencoder = FALSE, pretrained_autoencoder = NULL,
   use_all_factor_levels = TRUE, standardize = TRUE,
   activation = c("Rectifier", "Tanh", "TanhWithDropout",
   "RectifierWithDropout", "Maxout", "MaxoutWithDropout"), hidden = c(200,
   200), epochs = 10, train_samples_per_iteration = -2,
   target_ratio_comm_to_comp = 0.05, seed, adaptive_rate = TRUE,
   rho = 0.99, epsilon = 1e-08, rate = 0.005, rate_annealing = 1e-06,
   rate_decay = 1, momentum_start = 0, momentum_ramp = 1e+06,
   momentum_stable = 0, nesterov_accelerated_gradient = TRUE,
   input_dropout_ratio = 0, hidden_dropout_ratios, l1 = 0, l2 = 0,
   max_w2 = Inf, initial_weight_distribution = c("UniformAdaptive",
   "Uniform", "Normal"), initial_weight_scale = 1, initial_weights = NULL,
   initial_biases = NULL, loss = c("Automatic", "CrossEntropy", "Quadratic",
   "Absolute", "Huber"), distribution = c("AUTO", "gaussian", "bernoulli",
   "multinomial", "poisson", "gamma", "tweedie", "laplace", "huber", "quantile"),
   quantile_alpha = 0.5, tweedie_power = 1.5, huber_alpha,
   score_interval = 5, score_training_samples, score_validation_samples,
   score_duty_cycle, classification_stop, regression_stop, stopping_rounds = 5,
   stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "AUC", "r2",
   "misclassification", "mean_per_class_error"), stopping_tolerance = 0,
   max_runtime_secs = 0, quiet_mode, max_confusion_matrix_size,
   max_hit_ratio_k, balance_classes = FALSE, class_sampling_factors,
   max_after_balance_size, score_validation_sampling,
   missing_values_handling = c("MeanImputation", "Skip"), diagnostics,
   variable_importances, fast_mode, ignore_const_cols, force_load_balance,
   replicate_training_data, single_node_mode, shuffle_training_data, sparse,
   col_major, average_activation, sparsity_beta, max_categorical_features,
   categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit",
   "Binary", "Eigen"), reproducible = FALSE,
   export_weights_and_biases = FALSE, offset_column = NULL,
   weights_column = NULL, nfolds = 0, fold_column = NULL,
   fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
   keep_cross_validation_predictions = FALSE,
   keep_cross_validation_fold_assignment = FALSE)

Arguments:

   x: A vector containing the ‘character’ names of the predictors
      in the model. If x is missing,then all columns except y are
      used.

   y: The name of the response variable in the model.

training_frame: An H2OFrame object containing the variables in the model.

model_id: (Optional) The unique id assigned to the resulting model. If none is given, an id will automatically be generated. {code}

h2oai / h2o-3

Add function parameters with their defaults to all Python methods #10406