def test_inplace_prediction(test_dir, data_frame):
label_col = 'label'
df = data_frame(n_samples=100, label_col=label_col)
data_encoder_cols = [TfIdfEncoder('features')]
label_encoder_cols = [CategoricalEncoder(label_col)]
data_cols = [BowFeaturizer('features')]
output_path = os.path.join(test_dir, "tmp", "out")
imputer = Imputer(data_featurizers=data_cols,
label_encoders=label_encoder_cols,
data_encoders=data_encoder_cols,
output_path=output_path).fit(train_df=df, num_epochs=1)
predicted = imputer.predict(df, inplace=True)
assert predicted is df
def test_non_writable_output_path(test_dir, data_frame):
label_col = 'label'
df = data_frame(n_samples=100, label_col=label_col)
data_encoder_cols = [TfIdfEncoder('features')]
label_encoder_cols = [CategoricalEncoder(label_col)]
data_cols = [BowFeaturizer('features')]
output_path = os.path.join(test_dir, 'non_writable')
Imputer(data_featurizers=data_cols,
label_encoders=label_encoder_cols,
data_encoders=data_encoder_cols,
output_path=output_path).fit(train_df=df, num_epochs=1).save()
from datawig.utils import logger
try:
# make output dir of imputer read-only
os.chmod(output_path, S_IREAD | S_IXUSR)
# make log file read only
os.chmod(os.path.join(output_path, "imputer.log"), S_IREAD)
imputer = Imputer.load(output_path)
_ = imputer.predict(df)
logger.warning("this should not fail")
# remove log file
os.chmod(os.path.join(output_path, "imputer.log"),
S_IREAD | S_IXUSR | S_IWUSR)
os.chmod(output_path, S_IREAD | S_IXUSR | S_IWUSR)
os.remove(os.path.join(output_path, "imputer.log"))
# make output dir of imputer read-only
os.chmod(output_path, S_IREAD | S_IXUSR)
imputer = Imputer.load(output_path)
_ = imputer.predict(df)
logger.warning("this should not fail")
os.chmod(output_path, S_IREAD | S_IXUSR | S_IWUSR)
except Exception as e:
print(e)
pytest.fail("This invocation not raise any Exception")
def test_imputer_load_with_invalid_context(tmpdir, data_frame):
# on shared build-fleet tests fail with converting tmpdir to string
tmpdir = str(tmpdir)
feature = 'feature'
label = 'label'
df = data_frame(feature, label, n_samples=100)
# fit and output model + metrics to tmpdir
imputer = Imputer(data_featurizers=[BowFeaturizer(feature)],
label_encoders=[CategoricalEncoder(label)],
data_encoders=[BowEncoder(feature)],
output_path=tmpdir)
imputer.fit(train_df=df, num_epochs=1)
imputer.ctx = None
imputer.save()
imputer_deser = Imputer.load(tmpdir)
_ = imputer_deser.predict(df)
def test_explain_instance_without_label(test_dir, data_frame):
label_col = 'label'
df = data_frame(n_samples=100, label_col=label_col)
data_encoder_cols = [TfIdfEncoder('features')]
label_encoder_cols = [CategoricalEncoder(label_col)]
data_cols = [BowFeaturizer('features')]
output_path = os.path.join(test_dir, "tmp", "out")
imputer = Imputer(data_featurizers=data_cols,
label_encoders=label_encoder_cols,
data_encoders=data_encoder_cols,
output_path=output_path).fit(train_df=df, num_epochs=1)
assert imputer.is_explainable
instance = pd.Series({'features': 'some feature text'})
# explain_instance should not raise an exception
_ = imputer.explain_instance(instance)
assert True
def test_imputer_fit_fail_non_writable_output_dir(tmpdir, data_frame):
import stat
# on shared build-fleet tests fail with converting tmpdir to string
tmpdir = str(tmpdir)
feature = 'feature'
label = 'label'
df = data_frame(feature, label, n_samples=100)
# fit and output model + metrics to tmpdir
imputer = Imputer(data_featurizers=[BowFeaturizer(feature)],
label_encoders=[CategoricalEncoder(label)],
data_encoders=[BowEncoder(feature)],
output_path=tmpdir)
# make tmpdir read/exec-only by owner/group/others
os.chmod(
tmpdir, stat.S_IEXEC | stat.S_IXGRP | stat.S_IXOTH | stat.S_IREAD
| stat.S_IRGRP | stat.S_IROTH)
# fail if imputer.fit does not raise an AssertionError
with pytest.raises(AssertionError) as e:
imputer.fit(df, num_epochs=1)
def test_mxnet_module_wrapper(data_frame):
from datawig.imputer import _MXNetModule
import mxnet as mx
from datawig.iterators import ImputerIterDf
feature_col, label_col = "feature", "label"
df = data_frame(n_samples=100,
feature_col=feature_col,
label_col=label_col)
label_encoders = [CategoricalEncoder(label_col)]
data_encoders = [BowEncoder(feature_col)]
data_featurizers = [BowFeaturizer(feature_col, vocab_size=100)]
iter_train = ImputerIterDf(df, data_encoders, label_encoders)
mod = _MXNetModule(mx.current_context(),
label_encoders,
data_featurizers,
final_fc_hidden_units=[])(iter_train)
assert mod._label_names == [label_col]
assert mod.data_names == [feature_col]
# weights and biases
assert len(mod._arg_params) == 2
def test_imputer_unrepresentative_test_df():
"""
Tests whether the imputer runs through in cases when test data set (and hence metrics and precision/recall curves)
doesn't contain values present in training data
"""
# generate some random data
random_data = generate_string_data_frame(n_samples=100)
df_train, df_test, df_val = random_split(random_data, [.8, .1, .1])
excluded = df_train['labels'].values[0]
df_test = df_test[df_test['labels'] != excluded]
data_encoder_cols = [BowEncoder('features')]
label_encoder_cols = [CategoricalEncoder('labels')]
data_cols = [BowFeaturizer('features')]
output_path = os.path.join(dir_path, "resources", "tmp", "real_data_experiment")
imputer = Imputer(
data_featurizers=data_cols,
label_encoders=label_encoder_cols,
data_encoders=data_encoder_cols,
output_path=output_path
).fit(
train_df=df_train,
test_df=df_test,
num_epochs=10)
only_excluded_df = df_train[df_train['labels'] == excluded]
imputations = imputer.predict_above_precision(only_excluded_df,
precision_threshold=.99)['labels']
assert all([x == () for x in imputations])
shutil.rmtree(output_path)
def test_imputer_real_data_all_featurizers(test_dir, data_frame):
"""
Tests Imputer with sequential, bag-of-words and categorical variables as inputs
this could be run as part of integration test suite.
"""
feature_col = "string_feature"
categorical_col = "categorical_feature"
label_col = "label"
n_samples = 5000
num_labels = 3
seq_len = 20
vocab_size = int(2**10)
latent_dim = 30
embed_dim = 30
# generate some random data
random_data = data_frame(feature_col=feature_col,
label_col=label_col,
vocab_size=vocab_size,
num_labels=num_labels,
num_words=seq_len,
n_samples=n_samples)
# we use a the label prefixes as a dummy categorical input variable
random_data[categorical_col] = random_data[label_col].apply(
lambda x: x[:2])
df_train, df_test, df_val = random_split(random_data, [.8, .1, .1])
data_encoder_cols = [
BowEncoder(feature_col, feature_col + "_bow", max_tokens=vocab_size),
SequentialEncoder(feature_col,
feature_col + "_lstm",
max_tokens=vocab_size,
seq_len=seq_len),
CategoricalEncoder(categorical_col, max_tokens=num_labels)
]
label_encoder_cols = [CategoricalEncoder(label_col, max_tokens=num_labels)]
data_cols = [
BowFeaturizer(feature_col + "_bow", vocab_size=vocab_size),
LSTMFeaturizer(field_name=feature_col + "_lstm",
seq_len=seq_len,
latent_dim=latent_dim,
num_hidden=30,
embed_dim=embed_dim,
num_layers=2,
vocab_size=num_labels),
EmbeddingFeaturizer(field_name=categorical_col,
embed_dim=embed_dim,
vocab_size=num_labels)
]
output_path = os.path.join(test_dir, "tmp",
"imputer_experiment_synthetic_data")
num_epochs = 10
batch_size = 32
learning_rate = 1e-2
imputer = Imputer(data_featurizers=data_cols,
label_encoders=label_encoder_cols,
data_encoders=data_encoder_cols,
output_path=output_path).fit(train_df=df_train,
test_df=df_val,
learning_rate=learning_rate,
num_epochs=num_epochs,
batch_size=batch_size,
calibrate=False)
len_df_before_predict = len(df_test)
pred = imputer.transform(df_test)
assert len(pred[label_col]) == len_df_before_predict
assert sum(df_test[label_col].values == pred[label_col]) == len(df_test)
_ = imputer.predict_proba_top_k(df_test, top_k=2)
_, metrics = imputer.transform_and_compute_metrics(df_test)
assert metrics[label_col]['avg_f1'] > 0.9
deserialized = Imputer.load(imputer.output_path)
_, metrics_deserialized = deserialized.transform_and_compute_metrics(
df_test)
assert metrics_deserialized[label_col]['avg_f1'] > 0.9
# training on a small data set to get a imputer with low precision
not_so_precise_imputer = Imputer(data_featurizers=data_cols,
label_encoders=label_encoder_cols,
data_encoders=data_encoder_cols,
output_path=output_path).fit(
train_df=df_train[:50],
test_df=df_test,
learning_rate=learning_rate,
num_epochs=num_epochs,
batch_size=batch_size,
calibrate=False)
df_test = df_test.reset_index()
predictions_df = not_so_precise_imputer.predict(
df_test, precision_threshold=.5, imputation_suffix="_imputed")
assert predictions_df.columns.contains(label_col + "_imputed")
assert predictions_df.columns.contains(label_col + "_imputed_proba")
def test_imputer_duplicate_encoder_output_columns(test_dir, data_frame):
"""
Tests Imputer with sequential, bag-of-words and categorical variables as inputs
this could be run as part of integration test suite.
"""
feature_col = "string_feature"
categorical_col = "categorical_feature"
label_col = "label"
n_samples = 1000
num_labels = 10
seq_len = 100
vocab_size = int(2**10)
latent_dim = 30
embed_dim = 30
# generate some random data
random_data = data_frame(feature_col=feature_col,
label_col=label_col,
vocab_size=vocab_size,
num_labels=num_labels,
num_words=seq_len,
n_samples=n_samples)
# we use a the label prefixes as a dummy categorical input variable
random_data[categorical_col] = random_data[label_col].apply(
lambda x: x[:2])
df_train, df_test, df_val = random_split(random_data, [.8, .1, .1])
data_encoder_cols = [
BowEncoder(feature_col, feature_col, max_tokens=vocab_size),
SequentialEncoder(feature_col,
feature_col,
max_tokens=vocab_size,
seq_len=seq_len),
CategoricalEncoder(categorical_col, max_tokens=num_labels)
]
label_encoder_cols = [CategoricalEncoder(label_col, max_tokens=num_labels)]
data_cols = [
BowFeaturizer(feature_col, vocab_size=vocab_size),
LSTMFeaturizer(field_name=feature_col,
seq_len=seq_len,
latent_dim=latent_dim,
num_hidden=30,
embed_dim=embed_dim,
num_layers=2,
vocab_size=num_labels),
EmbeddingFeaturizer(field_name=categorical_col,
embed_dim=embed_dim,
vocab_size=num_labels)
]
output_path = os.path.join(test_dir, "tmp",
"imputer_experiment_synthetic_data")
num_epochs = 20
batch_size = 16
learning_rate = 1e-3
with pytest.raises(ValueError) as e:
imputer = Imputer(data_featurizers=data_cols,
label_encoders=label_encoder_cols,
data_encoders=data_encoder_cols,
output_path=output_path)
imputer.fit(train_df=df_train,
test_df=df_val,
learning_rate=learning_rate,
num_epochs=num_epochs,
batch_size=batch_size)
def test_automatic_calibration(data_frame):
"""
Fit model with all featurisers and assert
that calibration improves the expected calibration error.
"""
feature_col = "string_feature"
categorical_col = "categorical_feature"
label_col = "label"
n_samples = 2000
num_labels = 3
seq_len = 20
vocab_size = int(2**10)
latent_dim = 30
embed_dim = 30
# generate some random data
random_data = data_frame(feature_col=feature_col,
label_col=label_col,
vocab_size=vocab_size,
num_labels=num_labels,
num_words=seq_len,
n_samples=n_samples)
# we use a the label prefixes as a dummy categorical input variable
random_data[categorical_col] = random_data[label_col].apply(
lambda x: x[:2])
df_train, df_test, df_val = random_split(random_data, [.8, .1, .1])
data_encoder_cols = [
BowEncoder(feature_col, feature_col + "_bow", max_tokens=vocab_size),
SequentialEncoder(feature_col,
feature_col + "_lstm",
max_tokens=vocab_size,
seq_len=seq_len),
CategoricalEncoder(categorical_col, max_tokens=num_labels)
]
label_encoder_cols = [CategoricalEncoder(label_col, max_tokens=num_labels)]
data_cols = [
BowFeaturizer(feature_col + "_bow", vocab_size=vocab_size),
LSTMFeaturizer(field_name=feature_col + "_lstm",
seq_len=seq_len,
latent_dim=latent_dim,
num_hidden=30,
embed_dim=embed_dim,
num_layers=2,
vocab_size=num_labels),
EmbeddingFeaturizer(field_name=categorical_col,
embed_dim=embed_dim,
vocab_size=num_labels)
]
num_epochs = 20
batch_size = 32
learning_rate = 1e-2
imputer = Imputer(data_featurizers=data_cols,
label_encoders=label_encoder_cols,
data_encoders=data_encoder_cols).fit(
train_df=df_train,
test_df=df_val,
learning_rate=learning_rate,
num_epochs=num_epochs,
batch_size=batch_size)
assert imputer.calibration_info['ece_pre'] > imputer.calibration_info[
'ece_post']
