def test_majority_label_vote(self):
L = np.array([[0, 1, 0], [0, 1, 0], [1, 0, 0], [-1, -1, 1]])
ml_voter = MajorityLabelVoter()
Y_p = ml_voter.predict_proba(L)
Y_p_true = np.array([[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0]])
np.testing.assert_array_almost_equal(Y_p, Y_p_true)
def model_analysis(label_model: LabelModel,
training_set: pd.DataFrame,
L_train: np.ndarray,
L_test: np.ndarray,
Y_test: np.ndarray,
lfs: list,
output_file="output") -> None:
# TODO: consider using **kwargs instead of this painful list of arguments
"""Output analysis for the label model to a file
:param label_model: The current label model which we want to output analysis for
:type label_model: LabelModel
:param training_set: A dataframe containing the training dataset
:type training_set: pd.DataFrame
:param L_train: The matrix of labels generated by the labeling functions on the training data
:type L_train: np.ndarray
:param L_test: The matrix of labels generated bt the labeling functions on the testing data
:type L_test: np.ndarray
:param Y_test: Gold labels associated with data points in L_test
:type Y_test: np.ndarray
:param lfs: List of labeling functions
:type lfs: list
:param output_file: A path where the output file should be writtent to, defaults to `PROJECT_ROOT/output`
:type output_file: str, optional
"""
Y_train = label_model.predict_proba(L=L_train)
Y_pred = label_model.predict(L=L_test, tie_break_policy="abstain")
lf_analysis_train = LFAnalysis(L=L_train, lfs=lfs).lf_summary()
# TODO: Write this df to a output file. Ask Jennifer about how to handle this
print(lf_analysis_train)
# build majority label voter model
majority_model = MajorityLabelVoter()
majority_acc = majority_model.score(L=L_test,
Y=Y_test,
tie_break_policy="abstain",
metrics=["f1", "accuracy"])
label_model_acc = label_model.score(L=L_test,
Y=Y_test,
tie_break_policy="abstain",
metrics=["f1", "accuracy"])
# get precision and recall scores
p_score = precision_score(y_true=Y_test, y_pred=Y_pred, average='weighted')
r_score = recall_score(y_true=Y_test,
y_pred=Y_pred,
average='weighted',
labels=np.unique(Y_pred))
# how many documents abstained
probs_train = majority_model.predict_proba(L=L_train)
df_train_filtered, probs_train_filtered = filter_unlabeled_dataframe(
X=training_set, y=probs_train, L=L_train)
# get number of false positives
buckets = get_label_buckets(Y_test, Y_pred)
true_positives, false_positives, true_negatives, false_negatives = (
buckets.get((1, 1)), buckets.get((1, 0)), buckets.get(
(0, 0)), buckets.get((0, 1)))
# write analysis to file
timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
with open(f"{'../output/logs/'}{output_file}_run_{timestamp}.txt",
"w") as output_file:
output_file.write(
f"{'Majority Vote Accuracy:':<25} {majority_acc['accuracy'] * 100:.2f}%"
)
output_file.write(
f"\n{'Majority Vote F1 Score:':<25} {majority_acc['f1'] * 100:.2f}%"
)
output_file.write(
f"\n{'Label Model Accuracy:':<25} {label_model_acc['accuracy'] * 100:.2f}%"
)
output_file.write(
f"\n{'Label Model F1 Score:':<25} {label_model_acc['f1'] * 100:.2f}%"
)
output_file.write(f"\n{'Precision Score:':<25} {p_score * 100:.2f}%")
output_file.write(f"\n{'Recall Score:':<25} {r_score * 100:.2f}%")
output_file.write(
f"\n{'Abstained Data Points:':<25} {len(df_train_filtered)}")
output_file.write(
f"\n{'True Positives:':<25} {len(true_positives) if true_positives is not None else 0}"
)
output_file.write(
f"\n{'False Positives:':<25} {len(false_positives) if false_positives is not None else 0}"
)
output_file.write(
f"\n{'False Negatives:':<25} {len(false_negatives) if false_negatives is not None else 0}"
)
output_file.write(
f"\n{'True Negatives:':<25} {len(true_negatives) if true_negatives is not None else 0}"
)
output_file.write(
f"\n{'Abstained Positives:':<25} {len(buckets[(1, -1)])}")
output_file.write(
f"\n{'Abstained Negatives:':<25} {len(buckets[(0, -1)])}")
class_balance=[0.7, 0.3],
n_epochs=100)
# %%
Y_probs_valid = label_model.predict_proba(L_valid)
Y_preds_valid = probs_to_preds(Y_probs_valid)
metric_score(Y_valid, Y_preds_valid, probs=None, metric="f1")
# %% [markdown]
# **Majority Vote**
# %%
from snorkel.labeling.model import MajorityLabelVoter
mv_model = MajorityLabelVoter()
Y_probs_valid = mv_model.predict_proba(L_valid)
Y_preds_valid = probs_to_preds(Y_probs_valid)
metric_score(Y_valid, Y_preds_valid, probs=None, metric="f1")
# %%
# from metal.tuners import RandomSearchTuner
# # Creating search space
# search_space = {
# "l2": {"range": [0.0001, 0.1], "scale": "log"}, # linear range
# "lr": {"range": [0.0001, 0.1], "scale": "log"}, # log range
# }
# searcher = RandomSearchTuner(LabelModel, log_dir="./run_logs", log_writer_class=None)
# # Training label model
