changes to standard.

"""

import numpy as np

from deep_chem.utils.analysis import results_to_csv

from deep_chem.utils.load import load_and_transform_dataset

from deep_chem.utils.preprocess import split_dataset

from deep_chem.utils.preprocess import dataset_to_numpy

from deep_chem.utils.evaluate import eval_model

from sklearn.linear_model import LassoLarsCV

from sklearn.svm import SVR

def fit_singletask_models(per_task_data, modeltype, task_types,

    num_to_train=None):

def fit_singletask_models(per_task_data, modeltype, task_types):

  """Fits singletask linear regression models to potency.

  Parameters

    dict mapping target names to label transform. Each output type must be either

    None or "log". Only for regression outputs.

  """

  all_results = {}

  aucs, r2s, rms = {}, {}, {}

  sorted_targets = sorted(per_task_data.keys())

  if num_to_train:

    sorted_targets = sorted_targets[:num_to_train]

  for index, target in enumerate(sorted_targets):

  models = {}

  for index, target in enumerate(sorted(per_task_data.keys())):

    print "Building model %d" % index

    (train, X_train, y_train, W_train), (test, X_test, y_test, W_test) = (

        per_task_data[target])

    else:

      raise ValueError("Invalid model type provided.")

    model.fit(X_train, y_train.ravel())

    results = eval_model(test, model, {target: task_types[target]},

        modeltype="sklearn")

    all_results[target] = results[target]

    target_aucs = compute_roc_auc_scores(results, task_types)

    target_r2s = compute_r2_scores(results, task_types)

    target_rms = compute_rms_scores(results, task_types)

    aucs.update(target_aucs)

    r2s.update(target_r2s)

    rms.update(target_rms)

  if aucs:

    print results_to_csv(aucs)

    print "Mean AUC: %f" % np.mean(np.array(aucs.values()))

  if r2s:

    print results_to_csv(r2s)

    print "Mean R^2: %f" % np.mean(np.array(r2s.values()))

  if rms:

    print results_to_csv(rms)

    print "Mean RMS: %f" % np.mean(np.array(rms.values()))

  return all_results

    models[target] = model

  return models

# TODO(rbharath): I believe this is broken. Update it to work with the rest of

# the package.

def fit_multitask_rf(train_data, test_data, task_types):

  """Fits a multitask RF model to provided dataset.

  """