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modeler.py
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modeler.py
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import json
import numpy as np
import os
import pickle
import sys
from math import log
from sklearn import metrics
from sklearn.feature_extraction.text import CountVectorizer
from snorkel.labeling import LFAnalysis
from snorkel.labeling import LabelModel
from snorkel.labeling import PandasLFApplier
from snorkel.labeling import filter_unlabeled_dataframe
from snorkel.utils import preds_to_probs
class Modeler:
def __init__(self, df_train, df_dev, df_valid, df_test, df_heldout, lfs={}, label_model=None):
df_train["seen"] = 0
self.df_train = df_train.reset_index()
self.df_dev = df_dev
self.df_valid = df_valid
self.df_test = df_test
self.df_heldout = df_heldout
#self.Y_train = df_train.label.values
self.Y_dev = df_dev.label.values
self.Y_valid = df_valid.label.values
self.Y_test = df_test.label.values
self.Y_heldout = df_heldout.label.values
self.lfs = lfs
self.L_train = None
self.L_dev = None
self.L_valid = None
self.L_heldout = None
cardinality = len(df_valid.label.unique())
# for DEMOing purposes
self.first_text_indices = [
1262, #"check out" "youtube"
1892, # I love
1117, # url concept
1706, # emoji concept
952, # "nice"
971, # positive concept
958, # actually use emoji concept
]
self.count = 0
if label_model is None:
self.label_model = LabelModel(cardinality=cardinality, verbose=True)
else:
self.label_model = label_model
self.vectorizer = CountVectorizer(ngram_range=(1, 2))
self.vectorizer.fit(df_train.text.tolist())
def get_lfs(self):
return list(self.lfs.values())
def add_lfs(self, new_lfs: dict):
self.lfs.update(new_lfs)
def remove_lfs(self, old_lf_ids: list):
for lf_id in old_lf_ids:
del self.lfs[lf_id]
return len(self.lfs)
def apply_lfs(self):
applier = PandasLFApplier(lfs=self.get_lfs())
self.L_train = applier.apply(df=self.df_train)
self.L_dev = applier.apply(df=self.df_dev)
self.L_heldout = applier.apply(df=self.df_heldout)
#self.L_valid = applier.apply(df=self.df_valid)
def find_duplicate_signature(self):
label_matrix = np.vstack([self.L_train, self.L_dev])
seen_signatures = {}
dupes = {}
lfs = self.get_lfs()
signatures = [hash(label_matrix[:,i].tostring()) for i in range(len(lfs))]
for i, s in enumerate(signatures):
lf = lfs[i]
if s in seen_signatures:
dupes[lf.name] = seen_signatures[s]
else:
seen_signatures[s] = lf.name
return dupes
def lf_examples(self, lf_id, n=5):
lf = self.lfs[lf_id]
applier = PandasLFApplier(lfs=[lf])
L_train = applier.apply(df=self.df_train)
labeled_examples = self.df_train[L_train!=-1]
samples = labeled_examples.sample(min(n, len(labeled_examples)), random_state=13)
return [{"text": t} for t in samples["text"].values]
def lf_mistakes(self, lf_id, n=5):
lf = self.lfs[lf_id]
applier = PandasLFApplier(lfs=[lf])
L_dev = applier.apply(df=self.df_dev).squeeze()
labeled_examples = self.df_dev[(L_dev!=-1) & (L_dev != self.df_dev["label"])]
samples = labeled_examples.sample(min(n, len(labeled_examples)), random_state=13)
return [{"text": t} for t in samples["text"].values]
def fit_label_model(self):
assert self.L_train is not None
self.label_model.fit(L_train=self.L_train, n_epochs=1000, lr=0.001, log_freq=100, seed=123)
def analyze_lfs(self):
if len(self.lfs) > 0:
df = LFAnalysis(L=self.L_train, lfs=self.get_lfs()).lf_summary()
dev_df = LFAnalysis(L=self.L_dev, lfs=self.get_lfs()).lf_summary(Y=self.Y_dev)
df = df.merge(dev_df, how="outer", suffixes=(" Training", " Dev."), left_index=True, right_index=True)
df["Weight"] = self.label_model.get_weights()
df["Duplicate"] = None
for dupe, OG in self.find_duplicate_signature().items():
print("Duplicate labeling signature detected")
print(dupe, OG)
df.at[dupe, "Duplicate"] = OG
return df
return None
def get_label_model_stats(self):
result = self.label_model.score(L=self.L_dev, Y=self.Y_dev, metrics=["f1", "precision", "recall"])
probs_train = self.label_model.predict_proba(L=self.L_train)
df_train_filtered, probs_train_filtered = filter_unlabeled_dataframe(
X=self.df_train, y=probs_train, L=self.L_train
)
result["training_label_coverage"] = len(probs_train_filtered)/len(probs_train)
result["class_0_ratio"] = (probs_train_filtered[:,0]>0.5).sum()/len(probs_train_filtered)
if len(probs_train_filtered) == 0:
result["class_0_ratio"] = 0
return result
def get_heldout_stats(self):
if self.L_heldout is not None:
return self.label_model.score(L=self.L_heldout, Y=self.Y_heldout, metrics=["f1", "precision", "recall"])
return {}
def train(self):
probs_train = self.label_model.predict_proba(L=self.L_train)
df_train_filtered, probs_train_filtered = filter_unlabeled_dataframe(
X=self.df_train, y=probs_train, L=self.L_train
)
if len(df_train_filtered) == 0:
print("Labeling functions cover none of the training examples!", file=sys.stderr)
return {"micro_f1": 0}
#from tensorflow.keras.utils import to_categorical
#df_train_filtered, probs_train_filtered = self.df_dev, to_categorical(self.df_dev["label"].values)
vectorizer = self.vectorizer
X_train = vectorizer.transform(df_train_filtered.text.tolist())
X_dev = vectorizer.transform(self.df_dev.text.tolist())
X_valid = vectorizer.transform(self.df_valid.text.tolist())
X_test = vectorizer.transform(self.df_test.text.tolist())
self.keras_model = get_keras_logreg(input_dim=X_train.shape[1])
self.keras_model.fit(
x=X_train,
y=probs_train_filtered,
validation_data=(X_valid, preds_to_probs(self.Y_valid, 2)),
callbacks=[get_keras_early_stopping()],
epochs=20,
verbose=0,
)
preds_test = self.keras_model.predict(x=X_test).argmax(axis=1)
#return preds_test
return self.get_stats(self.Y_test, preds_test)
def get_heldout_lr_stats(self):
X_heldout = self.vectorizer.transform(self.df_heldout.text.tolist())
preds_test = self.keras_model.predict(x=X_heldout).argmax(axis=1)
return self.get_stats(self.Y_heldout, preds_test)
def get_stats(self, Y_test, preds_test):
label_classes = np.unique(self.Y_test)
accuracy = metrics.accuracy_score(Y_test, preds_test)
precision_0, precision_1 = metrics.precision_score(Y_test, preds_test, labels=label_classes, average=None)
recall_0, recall_1 = metrics.recall_score(Y_test, preds_test, labels=label_classes, average=None)
test_f1 = metrics.f1_score(Y_test, preds_test, labels=label_classes)
#recall_0, recall_1 = metrics.precision_recall_fscore_support(self.Y_test, preds_test, labels=label_classes)["recall"]
return {
"micro_f1": test_f1,
"recall_0": recall_0,
"precision_0": precision_0,
"accuracy": accuracy,
"recall_1": recall_1,
"precision_1": precision_1
}
def entropy(self, prob_dist):
#return(-(L_row_i==-1).sum())
return(-sum([x*log(x) for x in prob_dist]))
def save(self, dir_name):
self.label_model.save(os.path.join(dir_name, 'label_model.pkl'))
with open(os.path.join(dir_name, 'model_lfs.pkl'), "wb+") as file:
pickle.dump(self.lfs, file)
def load(self, dir_name):
with open(os.path.join(dir_name, 'model_lfs.pkl'), "rb") as file:
lfs = pickle.load(file)
label_model = LabelModel.load(os.path.join(dir_name, 'label_model.pkl'))
self.lfs = lfs
self.label_model = label_model