def test_hpo_numeric_best_pick(test_dir, data_frame):
feature_col, label_col = "feature", "label"
df = data_frame(feature_col=feature_col, label_col=label_col)
df.loc[:, label_col] = np.random.randn(df.shape[0])
imputer = SimpleImputer(input_columns=[feature_col],
output_column=label_col,
output_path=test_dir,
is_explainable=True)
hps = {feature_col: {'max_tokens': [1, 2, 3]}}
hps[feature_col]['tokens'] = [['chars']]
imputer.fit_hpo(df, hps=hps)
results = imputer.hpo.results
max_tokens_of_encoder = imputer.imputer.data_encoders[
0].vectorizer.max_features
# model with minimal MSE
best_hpo_run = imputer.hpo.results['mse'].astype('float').idxmin()
loaded_hpo_run = results.loc[results[feature_col + ':max_tokens'] ==
max_tokens_of_encoder].index[0]
assert best_hpo_run == loaded_hpo_run
def test_hpo_many_columns(test_dir, data_frame):
feature_col, label_col = "feature", "label"
n_samples = 300
num_labels = 3
ncols = 10
seq_len = 4
# generate some random data
df = data_frame(feature_col=feature_col,
label_col=label_col,
num_labels=num_labels,
num_words=seq_len,
n_samples=n_samples)
for col in range(ncols):
df[feature_col + '_' + str(col)] = df[feature_col]
imputer = SimpleImputer(
input_columns=[col for col in df.columns if not col in ['label']],
output_column=label_col,
output_path=test_dir)
imputer.fit_hpo(df, num_evals=2, num_epochs=10)
assert imputer.hpo.results.precision_weighted.max() > .75
def test_hpo_runs(test_dir, data_frame):
feature_col, label_col = "feature", "label"
df = data_frame(feature_col=feature_col, label_col=label_col)
imputer = SimpleImputer(
input_columns=[col for col in df.columns if col != label_col],
output_column=label_col,
output_path=test_dir)
hps = dict()
max_tokens = [1024, 2048]
hps[feature_col] = {'max_tokens': max_tokens}
hps['global'] = {}
hps['global']['concat_columns'] = [False]
hps['global']['num_epochs'] = [10]
hps['global']['num_epochs'] = [10]
hps['global']['num_epochs'] = [10]
imputer.fit_hpo(df,
hps=hps,
num_hash_bucket_candidates=[2**15],
tokens_candidates=['words'])
# only search over specified parameter ranges
assert set(
imputer.hpo.results[feature_col + ':' +
'max_tokens'].unique().tolist()) == set(max_tokens)
assert imputer.hpo.results.shape[0] == 2
def test_hpo_defaults(test_dir, data_frame):
label_col = "label"
n_samples = 500
num_labels = 3
seq_len = 10
# generate some random data
df = data_frame(feature_col="string_feature",
label_col=label_col,
num_labels=num_labels,
num_words=seq_len,
n_samples=n_samples)
# add categorical feature
df['categorical_feature'] = [
'foo' if r > .5 else 'bar' for r in np.random.rand(n_samples)
]
# add numerical feature
df['numeric_feature'] = np.random.rand(n_samples)
df_train, df_test = random_split(df, [.8, .2])
output_path = os.path.join(test_dir, "tmp",
"real_data_experiment_text_hpo")
imputer = SimpleImputer(input_columns=[
'string_feature', 'categorical_feature', 'numeric_feature'
],
output_column='label',
output_path=output_path)
imputer.fit_hpo(df_train, num_evals=10, num_epochs=5)
assert imputer.hpo.results.precision_weighted.max() > .9
def test_hpo_many_columns(test_dir, data_frame):
"""
"""
label_col = "label"
n_samples = 300
num_labels = 3
ncols = 10
seq_len = 4
# generate some random data
df = data_frame(feature_col="string_feature",
label_col=label_col,
num_labels=num_labels,
num_words=seq_len,
n_samples=n_samples)
for col in range(ncols):
df['string_featur_' + str(col)] = df['string_feature']
df_train, df_test = random_split(df, [.8, .2])
output_path = os.path.join(test_dir, "tmp",
"real_data_experiment_text_hpo")
imputer = SimpleImputer(
input_columns=[col for col in df.columns if not col in ['label']],
output_column='label',
output_path=output_path)
imputer.fit_hpo(df_train, num_evals=2)
assert imputer.hpo.results.precision_weighted.max() > .8
def test_hpo_mixed_hps_and_kwargs(test_dir, data_frame):
feature_col, label_col = "feature", "label"
df = data_frame(feature_col=feature_col, label_col=label_col)
imputer = SimpleImputer(input_columns=[feature_col],
output_column=label_col,
output_path=test_dir)
hps = {feature_col: {'max_tokens': [1024]}}
imputer.fit_hpo(df, hps=hps, learning_rate_candidates=[0.1])
assert imputer.hpo.results['global:learning_rate'].values[0] == 0.1
def test_hpo_num_evals_empty_hps(test_dir, data_frame):
feature_col, label_col = "feature", "label"
# generate some random data
df = data_frame(feature_col=feature_col, label_col=label_col)
imputer = SimpleImputer(
input_columns=[col for col in df.columns if col != label_col],
output_column=label_col,
output_path=test_dir)
num_evals = 2
imputer.fit_hpo(df, num_evals=num_evals, num_epochs=10)
assert imputer.hpo.results.shape[0] == 2
def test_hpo_num_evals_given_hps(test_dir, data_frame):
feature_col, label_col = "feature", "label"
# generate some random data
df = data_frame(feature_col=feature_col, label_col=label_col)
# assert that num_evals is an upper bound on the number of hpo runs
for num_evals in range(1, 3):
imputer = SimpleImputer(
input_columns=[col for col in df.columns if col != label_col],
output_column=label_col,
output_path=test_dir)
imputer.fit_hpo(df, num_evals=num_evals, num_epochs=5)
assert imputer.hpo.results.shape[0] == num_evals
def test_hpo_similar_input_col_mixed_types(test_dir, data_frame):
feature_col, label_col = "feature", "label"
numeric_col = "numeric_feature"
categorical_col = "categorical_col"
df = data_frame(feature_col=feature_col, label_col=label_col)
df.loc[:, numeric_col] = np.random.randn(df.shape[0])
df.loc[:, categorical_col] = np.random.randint(df.shape[0])
imputer = SimpleImputer(
input_columns=[feature_col, numeric_col, categorical_col],
output_column=label_col,
output_path=test_dir)
imputer.fit_hpo(df, num_epochs=10)
def test_hpo_kwargs_only_support(test_dir, data_frame):
feature_col, label_col = "feature", "label"
numeric_col = "numeric_feature"
df = data_frame(feature_col=feature_col,
label_col=label_col)
df.loc[:, numeric_col] = np.random.randn(df.shape[0])
imputer = SimpleImputer(
input_columns=[feature_col, numeric_col],
output_column=label_col,
output_path=test_dir
)
imputer.fit_hpo(
df,
num_epochs=1,
patience=1,
weight_decay=[0.001],
batch_size=320,
num_hash_bucket_candidates=[3],
tokens_candidates=['words'],
numeric_latent_dim_candidates=[1],
numeric_hidden_layers_candidates=[1],
final_fc_hidden_units=[[1]],
learning_rate_candidates=[0.1],
normalize_numeric=False
)
def assert_val(col, value):
assert imputer.hpo.results[col].values[0] == value
assert_val('global:num_epochs', 1)
assert_val('global:patience', 1)
assert_val('global:weight_decay', 0.001)
assert_val('global:batch_size', 320)
assert_val(feature_col + ':max_tokens', 3)
assert_val(feature_col + ':tokens', ['words'])
assert_val(numeric_col + ':numeric_latent_dim', 1)
assert_val(numeric_col + ':numeric_hidden_layers', 1)
assert_val('global:final_fc_hidden_units', [1])
assert_val('global:learning_rate', 0.1)
def test_imputer_hpo_text(test_dir, data_frame):
"""
Tests SimpleImputer HPO with text data and categorical imputations
"""
feature_col = "string_feature"
label_col = "label"
n_samples = 1000
num_labels = 3
seq_len = 20
# generate some random data
df = data_frame(feature_col=feature_col,
label_col=label_col,
num_labels=num_labels,
num_words=seq_len,
n_samples=n_samples)
df_train, df_test = random_split(df, [.8, .2])
output_path = os.path.join(test_dir, "tmp", "experiment_text_hpo")
imputer_string = SimpleImputer(
input_columns=[feature_col],
output_column=label_col,
output_path=output_path
)
hps = dict()
hps[feature_col] = {}
hps[feature_col]['type'] = ['string']
hps[feature_col]['tokens'] = [['words'], ['chars']]
hps['global'] = {}
hps['global']['final_fc_hidden_units'] = [[]]
hps['global']['learning_rate'] = [1e-3]
hps['global']['weight_decay'] = [0]
hps['global']['num_epochs'] = [30]
imputer_string.fit_hpo(df_train, hps=hps, num_epochs=10, num_evals=3)
assert max(imputer_string.hpo.results['f1_micro']) > 0.7
def test_hpo_num_evals_given_hps(test_dir, data_frame):
feature_col, label_col = "feature", "label"
# generate some random data
df = data_frame(feature_col=feature_col, label_col=label_col)
num_evals = 2
# assert that num_evals is an upper bound on the number of hpo runs
for n_max_tokens_to_try in range(1, 5):
imputer = SimpleImputer(
input_columns=[col for col in df.columns if col != label_col],
output_column=label_col,
output_path=test_dir)
hps = {feature_col: {'max_tokens': n_max_tokens_to_try * [10]}}
imputer.fit_hpo(df, hps=hps, num_evals=num_evals)
assert imputer.hpo.results.shape[0] == min(num_evals,
n_max_tokens_to_try)
def test_hpo_single_column_encoder_parameter(test_dir, data_frame):
feature_col, label_col = "feature", "label"
df = data_frame(feature_col=feature_col, label_col=label_col)
imputer = SimpleImputer(
input_columns=[col for col in df.columns if col != label_col],
output_column=label_col,
output_path=test_dir,
is_explainable=True)
hps = dict()
hps[feature_col] = {'max_tokens': [1024]}
hps['global'] = {}
hps['global']['num_epochs'] = [10]
imputer.fit_hpo(df, hps=hps)
assert imputer.hpo.results.shape[0] == 2
assert imputer.imputer.data_encoders[0].vectorizer.max_features == 1024
def test_hpo_multiple_columns_only_one_used(test_dir, data_frame):
feature_col, label_col = "feature", "label"
df = data_frame(feature_col=feature_col, label_col=label_col)
df.loc[:, feature_col + '_2'] = df.loc[:, feature_col]
imputer = SimpleImputer(input_columns=[feature_col],
output_column=label_col,
output_path=test_dir,
is_explainable=True)
hps = dict()
hps[feature_col] = {'max_tokens': [1024]}
hps['global'] = {}
hps['global']['num_epochs'] = [10]
imputer.fit_hpo(df, hps=hps)
assert imputer.hpo.results.shape[0] == 1
assert imputer.imputer.data_encoders[0].vectorizer.max_features == 1024
def test_imputer_hpo_numeric(test_dir):
"""
Tests SimpleImputer HPO for numeric data/imputation
"""
N = 200
numeric_data = np.random.uniform(-np.pi, np.pi, (N, ))
df = pd.DataFrame({
'x': numeric_data,
'**2': numeric_data**2 + np.random.normal(0, .1, (N, )),
})
df_train, df_test = random_split(df, [.8, .2])
output_path = os.path.join(test_dir, "tmp", "experiment_numeric_hpo")
imputer_numeric = SimpleImputer(input_columns=['x'],
output_column="**2",
output_path=output_path)
feature_col = 'x'
hps = {}
hps[feature_col] = {}
hps[feature_col]['type'] = ['numeric']
hps[feature_col]['numeric_latent_dim'] = [30]
hps[feature_col]['numeric_hidden_layers'] = [1]
hps['global'] = {}
hps['global']['final_fc_hidden_units'] = [[]]
hps['global']['learning_rate'] = [1e-3, 1e-4]
hps['global']['weight_decay'] = [0]
hps['global']['num_epochs'] = [200]
hps['global']['patience'] = [100]
hps['global']['concat_columns'] = [False]
imputer_numeric.fit_hpo(df_train, hps=hps)
results = imputer_numeric.hpo.results
assert results[results['mse'] == min(results['mse'])]['mse'].iloc[0] < .3
def test_imputer_hpo_text(test_dir, data_frame):
"""
Tests SimpleImputer HPO with text data and categorical imputations
"""
feature_col = "string_feature"
label_col = "label"
n_samples = 1000
num_labels = 3
seq_len = 20
# generate some random data
df = data_frame(feature_col=feature_col,
label_col=label_col,
num_labels=num_labels,
num_words=seq_len,
n_samples=n_samples)
df_train, df_test = random_split(df, [.8, .2])
output_path = os.path.join(test_dir, "tmp", "real_data_experiment_text_hpo")
imputer_string = SimpleImputer(
input_columns=[feature_col],
output_column=label_col,
output_path=output_path
)
imputer_string.fit_hpo(
train_df=df_train,
num_epochs=100,
patience=3,
num_hash_bucket_candidates=[2 ** 10, 2 ** 15],
tokens_candidates=['words'],
numeric_latent_dim_candidates=[10],
hpo_max_train_samples=1000
)
imputer_string.predict(df_test, inplace=True)
assert f1_score(df_test[label_col], df_test[label_col + '_imputed'], average="weighted") > .7
def test_imputer_hpo_numeric(test_dir):
"""
Tests SimpleImputer HPO for numeric data/imputation
"""
N = 200
numeric_data = np.random.uniform(-np.pi, np.pi, (N,))
df = pd.DataFrame({
'x': numeric_data,
'**2': numeric_data ** 2 + np.random.normal(0, .1, (N,)),
})
df_train, df_test = random_split(df, [.8, .2])
output_path = os.path.join(test_dir, "tmp", "real_data_experiment_numeric_hpo")
imputer_numeric = SimpleImputer(
input_columns=['x'],
output_column="**2",
output_path=output_path
)
imputer_numeric.fit_hpo(
train_df=df_train,
learning_rate=1e-3,
num_epochs=100,
patience=10,
num_hash_bucket_candidates=[2 ** 10],
tokens_candidates=['words'],
numeric_latent_dim_candidates=[10, 50, 100],
numeric_hidden_layers_candidates=[1, 2]
)
imputer_numeric.predict(df_test, inplace=True)
assert mean_squared_error(df_test['**2'], df_test['**2_imputed']) < 1.0
def test_hpo_all_input_types(test_dir, data_frame):
"""
Using sklearn advantages: parallelism, distributions of parameters, multiple cross-validation
"""
label_col = "label"
n_samples = 1000
num_labels = 3
seq_len = 12
# generate some random data
df = data_frame(feature_col="string_feature",
label_col=label_col,
num_labels=num_labels,
num_words=seq_len,
n_samples=n_samples)
# add categorical feature
df['categorical_feature'] = [
'foo' if r > .5 else 'bar' for r in np.random.rand(n_samples)
]
# add numerical feature
df['numeric_feature'] = np.random.rand(n_samples)
df_train, df_test = random_split(df, [.8, .2])
output_path = os.path.join(test_dir, "tmp",
"real_data_experiment_text_hpo")
imputer = SimpleImputer(input_columns=[
'string_feature', 'categorical_feature', 'numeric_feature'
],
output_column='label',
output_path=output_path)
# Define default hyperparameter choices for each column type (string, categorical, numeric)
hps = dict()
hps['global'] = {}
hps['global']['learning_rate'] = [3e-4]
hps['global']['weight_decay'] = [1e-8]
hps['global']['num_epochs'] = [5, 50]
hps['global']['patience'] = [5]
hps['global']['batch_size'] = [16]
hps['global']['final_fc_hidden_units'] = [[]]
hps['global']['concat_columns'] = [True, False]
hps['string_feature'] = {}
hps['string_feature']['max_tokens'] = [2**15]
hps['string_feature']['tokens'] = [['words', 'chars']]
hps['string_feature']['ngram_range'] = {}
hps['string_feature']['ngram_range']['words'] = [(1, 4), (2, 5)]
hps['string_feature']['ngram_range']['chars'] = [(2, 4), (3, 5)]
hps['categorical_feature'] = {}
hps['categorical_feature']['type'] = ['categorical']
hps['categorical_feature']['max_tokens'] = [2**15]
hps['categorical_feature']['embed_dim'] = [10]
hps['numeric_feature'] = {}
hps['numeric_feature']['normalize'] = [True]
hps['numeric_feature']['numeric_latent_dim'] = [10]
hps['numeric_feature']['numeric_hidden_layers'] = [1]
# user defined score function for hyperparameters
def calibration_check(true, predicted, confidence):
"""
expect kwargs: true, predicted, confidence
here we compute a calibration sanity check
"""
return (np.mean(true[confidence > .9] == predicted[confidence > .9]),
np.mean(true[confidence > .5] == predicted[confidence > .5]))
def coverage_check(true, predicted, confidence):
return np.mean(confidence > .9)
uds = [(calibration_check, 'calibration check'),
(coverage_check, 'coverage at 90')]
imputer.fit_hpo(df_train,
hps=hps,
user_defined_scores=uds,
num_evals=5,
hpo_run_name='test1_')
imputer.fit_hpo(df_train,
hps=hps,
user_defined_scores=uds,
num_evals=5,
hpo_run_name='test2_',
max_running_hours=1 / 3600)
results = imputer.hpo.results
assert results[results['global:num_epochs'] == 50]['f1_micro'].iloc[0] > \
results[results['global:num_epochs'] == 5]['f1_micro'].iloc[0]