Exemplo n.º 1
0
  def test_y_log_transformer_select(self):
    """Tests logarithmic data transformer with selection."""
    multitask_dataset = self.load_feat_multitask_data()
    dfe = pd.read_csv(os.path.join(self.current_dir,
                      "../../models/tests/feat_multitask_example.csv"))
    tid = []
    tasklist =  ["task0", "task3", "task4", "task5"]
    first_task = "task0"
    for task in tasklist:
      tiid = dfe.columns.get_loc(task)-dfe.columns.get_loc(first_task)
      tid = np.concatenate((tid, np.array([tiid])))
    tasks = tid.astype(int)
    log_transformer = LogTransformer(
        transform_y=True, tasks=tasks,
        dataset=multitask_dataset)
    X, y, w, ids = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
    log_transformer.transform(multitask_dataset)
    X_t, y_t, w_t, ids_t = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)

    # Check ids are unchanged.
    for id_elt, id_t_elt in zip(ids, ids_t):
      assert id_elt == id_t_elt
    # Check X is unchanged since this is a y transformer
    np.testing.assert_allclose(X, X_t)
    # Check w is unchanged since this is a y transformer
    np.testing.assert_allclose(w, w_t)
    # Check y is now a logarithmic version of itself
    np.testing.assert_allclose(y_t[:,tasks], np.log(y[:,tasks]+1))

    # Check that untransform does the right thing.
    np.testing.assert_allclose(log_transformer.untransform(y_t), y)
Exemplo n.º 2
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  def test_X_log_transformer_select(self):
    #Tests logarithmic data transformer with selection.
    multitask_dataset = self.load_feat_multitask_data()
    dfe = pd.read_csv(os.path.join(self.current_dir,
                      "../../models/tests/feat_multitask_example.csv"))
    fid = []
    featurelist =  ["feat0", "feat1", "feat2","feat3", "feat5"]
    first_feature = "feat0"
    for feature in featurelist:
      fiid = dfe.columns.get_loc(feature)-dfe.columns.get_loc(first_feature)
      fid = np.concatenate((fid, np.array([fiid])))
    features = fid.astype(int)
    log_transformer = LogTransformer(
        transform_X=True, features=features,
        dataset=multitask_dataset)
    X, y, w, ids = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
    log_transformer.transform(multitask_dataset)
    X_t, y_t, w_t, ids_t = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)

    # Check ids are unchanged.
    for id_elt, id_t_elt in zip(ids, ids_t):
      assert id_elt == id_t_elt
    # Check y is unchanged since this is a X transformer
    np.testing.assert_allclose(y, y_t)
    # Check w is unchanged since this is a y transformer
    np.testing.assert_allclose(w, w_t)
    # Check y is now a logarithmic version of itself
    np.testing.assert_allclose(X_t[:,features], np.log(X[:,features]+1))

    # Check that untransform does the right thing.
    np.testing.assert_allclose(log_transformer.untransform(X_t), X)
Exemplo n.º 3
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  def test_X_log_transformer_select(self):
    #Tests logarithmic data transformer with selection.
    multitask_dataset = self.load_feat_multitask_data()
    dfe = pd.read_csv(os.path.join(self.current_dir,
                      "../../models/tests/feat_multitask_example.csv"))
    fid = []
    featurelist =  ["feat0", "feat1", "feat2","feat3", "feat5"]
    first_feature = "feat0"
    for feature in featurelist:
      fiid = dfe.columns.get_loc(feature)-dfe.columns.get_loc(first_feature)
      fid = np.concatenate((fid, np.array([fiid])))
    features = fid.astype(int)
    log_transformer = LogTransformer(
        transform_X=True, features=features,
        dataset=multitask_dataset)
    X, y, w, ids = multitask_dataset.to_numpy()
    log_transformer.transform(multitask_dataset)
    X_t, y_t, w_t, ids_t = multitask_dataset.to_numpy()

    # Check ids are unchanged.
    for id_elt, id_t_elt in zip(ids, ids_t):
      assert id_elt == id_t_elt
    # Check y is unchanged since this is a X transformer
    np.testing.assert_allclose(y, y_t)
    # Check w is unchanged since this is a y transformer
    np.testing.assert_allclose(w, w_t)
    # Check y is now a logarithmic version of itself
    np.testing.assert_allclose(X_t[:,features], np.log(X[:,features]+1))

    # Check that untransform does the right thing.
    np.testing.assert_allclose(log_transformer.untransform(X_t), X)
Exemplo n.º 4
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  def test_y_log_transformer_select(self):
    """Tests logarithmic data transformer with selection."""
    multitask_dataset = self.load_feat_multitask_data()
    dfe = pd.read_csv(os.path.join(self.current_dir,
                      "../../models/tests/feat_multitask_example.csv"))
    tid = []
    tasklist =  ["task0", "task3", "task4", "task5"]
    first_task = "task0"
    for task in tasklist:
      tiid = dfe.columns.get_loc(task)-dfe.columns.get_loc(first_task)
      tid = np.concatenate((tid, np.array([tiid])))
    tasks = tid.astype(int)
    log_transformer = LogTransformer(
        transform_y=True, tasks=tasks,
        dataset=multitask_dataset)
    X, y, w, ids = multitask_dataset.to_numpy()
    log_transformer.transform(multitask_dataset)
    X_t, y_t, w_t, ids_t = multitask_dataset.to_numpy()

    # Check ids are unchanged.
    for id_elt, id_t_elt in zip(ids, ids_t):
      assert id_elt == id_t_elt
    # Check X is unchanged since this is a y transformer
    np.testing.assert_allclose(X, X_t)
    # Check w is unchanged since this is a y transformer
    np.testing.assert_allclose(w, w_t)
    # Check y is now a logarithmic version of itself
    np.testing.assert_allclose(y_t[:,tasks], np.log(y[:,tasks]+1))

    # Check that untransform does the right thing.
    np.testing.assert_allclose(log_transformer.untransform(y_t), y)
Exemplo n.º 5
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  def test_X_log_transformer(self):
    """Tests logarithmic data transformer."""
    solubility_dataset = self.load_solubility_data()
    log_transformer = LogTransformer(
        transform_X=True, dataset=solubility_dataset)
    X, y, w, ids = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
    log_transformer.transform(solubility_dataset)
    X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
    
    # Check ids are unchanged.
    for id_elt, id_t_elt in zip(ids, ids_t):
      assert id_elt == id_t_elt
    # Check y is unchanged since this is a X transformer
    np.testing.assert_allclose(y, y_t)
    # Check w is unchanged since this is a y transformer
    np.testing.assert_allclose(w, w_t)
    # Check y is now a logarithmic version of itself
    np.testing.assert_allclose(X_t, np.log(X+1))

    # Check that untransform does the right thing.
    np.testing.assert_allclose(log_transformer.untransform(X_t), X)
Exemplo n.º 6
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  def test_X_log_transformer(self):
    """Tests logarithmic data transformer."""
    solubility_dataset = self.load_solubility_data()
    log_transformer = LogTransformer(
        transform_X=True, dataset=solubility_dataset)
    X, y, w, ids = solubility_dataset.to_numpy()
    log_transformer.transform(solubility_dataset)
    X_t, y_t, w_t, ids_t = solubility_dataset.to_numpy()
    
    # Check ids are unchanged.
    for id_elt, id_t_elt in zip(ids, ids_t):
      assert id_elt == id_t_elt
    # Check y is unchanged since this is a X transformer
    np.testing.assert_allclose(y, y_t)
    # Check w is unchanged since this is a y transformer
    np.testing.assert_allclose(w, w_t)
    # Check y is now a logarithmic version of itself
    np.testing.assert_allclose(X_t, np.log(X+1))

    # Check that untransform does the right thing.
    np.testing.assert_allclose(log_transformer.untransform(X_t), X)