SYMBOL INDEX (110 symbols across 8 files)

FILE: src/base_models.py
  class NeuralNetwork (line 5) | class NeuralNetwork(nn.Module):
    method __init__ (line 6) | def __init__(self, in_size=50, hidden_size=1000, out_size=1):
    method forward (line 15) | def forward(self, x):
  class ParallelNetworks (line 20) | class ParallelNetworks(nn.Module):
    method __init__ (line 21) | def __init__(self, num_models, model_class, **model_class_init_args):
    method forward (line 27) | def forward(self, xs):

FILE: src/curriculum.py
  class Curriculum (line 4) | class Curriculum:
    method __init__ (line 5) | def __init__(self, args):
    method update (line 16) | def update(self):
    method update_var (line 23) | def update_var(self, var, schedule):
  function get_final_var (line 31) | def get_final_var(init_var, total_steps, inc, n_steps, lim):

FILE: src/eval.py
  function get_model_from_run (line 17) | def get_model_from_run(run_path, step=-1, only_conf=False):
  function eval_batch (line 41) | def eval_batch(model, task_sampler, xs, xs_p=None):
  function gen_standard (line 68) | def gen_standard(data_sampler, n_points, b_size):
  function gen_opposite_quadrants (line 74) | def gen_opposite_quadrants(data_sampler, n_points, b_size):
  function gen_random_quadrants (line 84) | def gen_random_quadrants(data_sampler, n_points, b_size):
  function gen_orthogonal_train_test (line 94) | def gen_orthogonal_train_test(data_sampler, n_points, b_size):
  function gen_overlapping_train_test (line 120) | def gen_overlapping_train_test(data_sampler, n_points, b_size):
  function aggregate_metrics (line 134) | def aggregate_metrics(metrics, bootstrap_trials=1000):
  function eval_model (line 151) | def eval_model(
  function build_evals (line 192) | def build_evals(conf):
  function compute_evals (line 265) | def compute_evals(all_models, evaluation_kwargs, save_path=None, recompu...
  function get_run_metrics (line 290) | def get_run_metrics(
  function conf_to_model_name (line 323) | def conf_to_model_name(conf):
  function baseline_names (line 334) | def baseline_names(name):
  function read_run_dir (line 354) | def read_run_dir(run_dir):

FILE: src/models.py
  function build_model (line 14) | def build_model(conf):
  function get_relevant_baselines (line 29) | def get_relevant_baselines(task_name):
  class TransformerModel (line 80) | class TransformerModel(nn.Module):
    method __init__ (line 81) | def __init__(self, n_dims, n_positions, n_embd=128, n_layer=12, n_head...
    method _combine (line 102) | def _combine(xs_b, ys_b):
    method forward (line 116) | def forward(self, xs, ys, inds=None):
  class NNModel (line 130) | class NNModel:
    method __init__ (line 131) | def __init__(self, n_neighbors, weights="uniform"):
    method __call__ (line 137) | def __call__(self, xs, ys, inds=None):
  class LeastSquaresModel (line 174) | class LeastSquaresModel:
    method __init__ (line 175) | def __init__(self, driver=None):
    method __call__ (line 179) | def __call__(self, xs, ys, inds=None):
  class AveragingModel (line 206) | class AveragingModel:
    method __init__ (line 207) | def __init__(self):
    method __call__ (line 210) | def __call__(self, xs, ys, inds=None):
  class LassoModel (line 236) | class LassoModel:
    method __init__ (line 237) | def __init__(self, alpha, max_iter=100000):
    method __call__ (line 245) | def __call__(self, xs, ys, inds=None):
  class GDModel (line 294) | class GDModel:
    method __init__ (line 295) | def __init__(
    method __call__ (line 320) | def __call__(self, xs, ys, inds=None, verbose=False, print_step=100):
  class DecisionTreeModel (line 403) | class DecisionTreeModel:
    method __init__ (line 404) | def __init__(self, max_depth=None):
    method __call__ (line 410) | def __call__(self, xs, ys, inds=None):
  class XGBoostModel (line 442) | class XGBoostModel:
    method __init__ (line 443) | def __init__(self):
    method __call__ (line 448) | def __call__(self, xs, ys, inds=None):

FILE: src/plot_utils.py
  function basic_plot (line 43) | def basic_plot(metrics, models=None, trivial=1.0):
  function collect_results (line 70) | def collect_results(run_dir, df, valid_row=None, rename_eval=None, renam...

FILE: src/samplers.py
  class DataSampler (line 6) | class DataSampler:
    method __init__ (line 7) | def __init__(self, n_dims):
    method sample_xs (line 10) | def sample_xs(self):
  function get_data_sampler (line 14) | def get_data_sampler(data_name, n_dims, **kwargs):
  function sample_transformation (line 26) | def sample_transformation(eigenvalues, normalize=False):
  class GaussianSampler (line 36) | class GaussianSampler(DataSampler):
    method __init__ (line 37) | def __init__(self, n_dims, bias=None, scale=None):
    method sample_xs (line 42) | def sample_xs(self, n_points, b_size, n_dims_truncated=None, seeds=None):

FILE: src/tasks.py
  function squared_error (line 6) | def squared_error(ys_pred, ys):
  function mean_squared_error (line 10) | def mean_squared_error(ys_pred, ys):
  function accuracy (line 14) | def accuracy(ys_pred, ys):
  function cross_entropy (line 22) | def cross_entropy(ys_pred, ys):
  class Task (line 28) | class Task:
    method __init__ (line 29) | def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None):
    method evaluate (line 36) | def evaluate(self, xs):
    method generate_pool_dict (line 40) | def generate_pool_dict(n_dims, num_tasks):
    method get_metric (line 44) | def get_metric():
    method get_training_metric (line 48) | def get_training_metric():
  function get_task_sampler (line 52) | def get_task_sampler(
  class LinearRegression (line 76) | class LinearRegression(Task):
    method __init__ (line 77) | def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, sca...
    method evaluate (line 96) | def evaluate(self, xs_b):
    method generate_pool_dict (line 102) | def generate_pool_dict(n_dims, num_tasks, **kwargs):  # ignore extra args
    method get_metric (line 106) | def get_metric():
    method get_training_metric (line 110) | def get_training_metric():
  class SparseLinearRegression (line 114) | class SparseLinearRegression(LinearRegression):
    method __init__ (line 115) | def __init__(
    method evaluate (line 145) | def evaluate(self, xs_b):
    method get_metric (line 151) | def get_metric():
    method get_training_metric (line 155) | def get_training_metric():
  class LinearClassification (line 159) | class LinearClassification(LinearRegression):
    method evaluate (line 160) | def evaluate(self, xs_b):
    method get_metric (line 165) | def get_metric():
    method get_training_metric (line 169) | def get_training_metric():
  class NoisyLinearRegression (line 173) | class NoisyLinearRegression(LinearRegression):
    method __init__ (line 174) | def __init__(
    method evaluate (line 191) | def evaluate(self, xs_b):
  class QuadraticRegression (line 200) | class QuadraticRegression(LinearRegression):
    method evaluate (line 201) | def evaluate(self, xs_b):
  class Relu2nnRegression (line 211) | class Relu2nnRegression(Task):
    method __init__ (line 212) | def __init__(
    method evaluate (line 247) | def evaluate(self, xs_b):
    method generate_pool_dict (line 258) | def generate_pool_dict(n_dims, num_tasks, hidden_layer_size=4, **kwargs):
    method get_metric (line 265) | def get_metric():
    method get_training_metric (line 269) | def get_training_metric():
  class DecisionTree (line 273) | class DecisionTree(Task):
    method __init__ (line 274) | def __init__(self, n_dims, batch_size, pool_dict=None, seeds=None, dep...
    method evaluate (line 310) | def evaluate(self, xs_b):
    method generate_pool_dict (line 335) | def generate_pool_dict(n_dims, num_tasks, hidden_layer_size=4, **kwargs):
    method get_metric (line 339) | def get_metric():
    method get_training_metric (line 343) | def get_training_metric():

FILE: src/train.py
  function train_step (line 22) | def train_step(model, xs, ys, optimizer, loss_func):
  function sample_seeds (line 31) | def sample_seeds(total_seeds, count):
  function train (line 38) | def train(model, args):
  function main (line 137) | def main(args):