# %% [markdown]
# # Crowdsourcing Tutorial

# %% [markdown]
# In this tutorial, we'll provide a simple walkthrough of how to use Snorkel in conjunction with crowdsourcing to create a training set for a sentiment analysis task.
# We already have crowdsourced labels for about half of the training dataset.
# The crowdsourced labels are fairly accurate, but do not cover the entire training dataset, nor are they available for the test set or during inference.
# To make up for their lack of training set coverage, we combine crowdsourced labels with heuristic labeling functions to increase the number of training labels we have.
# Like most Snorkel labeling pipelines, we'll use the denoised labels to train a deep learning
# model which can be applied to new, unseen data to automatically make predictions.

# %% [markdown]
# ## Dataset Details

# %% [markdown]
# In this tutorial, we'll use the [Weather Sentiment](https://data.world/crowdflower/weather-sentiment) dataset from Figure Eight.
# Our goal is to train a classifier that can label new tweets as expressing either a positive or negative sentiment.
#
# Crowdworkers were asked to label the sentiment of a particular tweet relating to the weather.
# The catch is that 20 crowdworkers graded each tweet, and in many cases crowdworkers assigned conflicting sentiment labels to the same tweet.
# This is a common issue when dealing with crowdsourced labeling workloads.
#
# Label options were positive, negative, or one of three other options saying they weren't sure if it was positive or negative; we use only the positive/negative labels.
# We've also altered the dataset to reflect a realistic crowdsourcing pipeline where only a subset of our available training set has received crowd labels.
#
# We will treat each crowdworker's labels as coming from a single labeling function (LF).
# This will allow us to learn a weight for how much to trust the labels from each crowdworker.
# We will also write a few heuristic labeling functions to cover the data points without crowd labels.
# Snorkel's ability to build high-quality datasets from multiple noisy labeling signals makes it an ideal framework to approach this problem.

# %% [markdown]
# ## Loading Crowdsourcing Dataset

# %% [markdown]
# We start by loading our data which has 287 data points in total.
# We take 50 for our development set and 50 for our test set.
# The remaining 187 data points form our training set.
# Since the dataset is already small, we skip using a validation set.
# Note that this very small dataset is primarily used for demonstration purposes here.
# In a real setting, we would expect to have access to many more unlabeled tweets, which could help us to train a higher quality model.

# %% {"tags": ["md-exclude"]}
import os

# Make sure we're in the right directory
if os.path.basename(os.getcwd()) == "snorkel-tutorials":
    os.chdir("crowdsourcing")

# %%
from data import load_data

crowd_labels, df_train, df_dev, df_test = load_data()
Y_dev = df_dev.sentiment.values
Y_test = df_test.sentiment.values

# %% [markdown] {"tags": ["md-exclude"]}
# First, let's take a look at our development set to get a sense of what the tweets look like.
# We use the following label convention: 0 = Negative, 1 = Positive.

# %% {"tags": ["md-exclude"]}
import pandas as pd

# Don't truncate text fields in the display
pd.set_option("display.max_colwidth", 0)

df_dev.head()

# %% [markdown] {"tags": ["md-exclude"]}
# Now let's take a look at the crowd labels.
# We'll convert these into labeling functions.

# %% {"tags": ["md-exclude"]}
crowd_labels.head()

# %% [markdown]
# ## Writing Labeling Functions
# Each crowdworker can be thought of as a single labeling function,
# as each worker labels a subset of data points,
# and may have errors or conflicting labels with other workers / labeling functions.
# So we create one labeling function per worker.
# We'll simply return the label the worker submitted for a given tweet, and abstain
# if they didn't submit a label for it.

# %% [markdown]
# ### Crowdworker labeling functions

# %%
labels_by_annotator = crowd_labels.groupby("worker_id")
worker_dicts = {}
for worker_id in labels_by_annotator.groups:
    worker_df = labels_by_annotator.get_group(worker_id)[["label"]]
    worker_dicts[worker_id] = dict(zip(worker_df.index, worker_df.label))

print("Number of workers:", len(worker_dicts))

# %%
from snorkel.labeling import LabelingFunction

ABSTAIN = -1


def worker_lf(x, worker_dict):
    return worker_dict.get(x.tweet_id, ABSTAIN)


def make_worker_lf(worker_id):
    worker_dict = worker_dicts[worker_id]
    name = f"worker_{worker_id}"
    return LabelingFunction(name, f=worker_lf, resources={"worker_dict": worker_dict})


worker_lfs = [make_worker_lf(worker_id) for worker_id in worker_dicts]

# %% [markdown]
# Let's take a quick look at how well they do on the development set.

# %% {"tags": ["md-exclude-output"]}
from snorkel.labeling import PandasLFApplier

applier = PandasLFApplier(worker_lfs)
L_train = applier.apply(df_train)
L_dev = applier.apply(df_dev)

# %% [markdown]
# Note that because our dev set is so small and our LFs are relatively sparse, many LFs will appear to have zero coverage.
# Fortunately, our label model learns weights for LFs based on their outputs on the training set, which is generally much larger.

# %%
from snorkel.labeling import LFAnalysis

LFAnalysis(L_dev, worker_lfs).lf_summary(Y_dev).sample(5)

# %% [markdown]
# So the crowd labels in general are quite good! But how much of our dev and training
# sets do they cover?

# %%
print(f"Training set coverage: {100 * LFAnalysis(L_train).label_coverage(): 0.1f}%")
print(f"Dev set coverage: {100 * LFAnalysis(L_dev).label_coverage(): 0.1f}%")

# %% [markdown]
# ### Additional labeling functions
#
# To improve coverage of the training set, we can mix the crowdworker labeling functions with labeling
# functions of other types.
# For example, we can use [TextBlob](https://textblob.readthedocs.io/en/dev/index.html), a tool that provides a pretrained sentiment analyzer. We run TextBlob on our tweets and create some simple LFs that threshold its polarity score, similar to what we did in the spam_tutorial.

# %%
from snorkel.labeling import labeling_function
from snorkel.preprocess import preprocessor
from textblob import TextBlob


@preprocessor(memoize=True)
def textblob_polarity(x):
    scores = TextBlob(x.tweet_text)
    x.polarity = scores.polarity
    return x


# Label high polarity tweets as positive.
@labeling_function(pre=[textblob_polarity])
def polarity_positive(x):
    return 1 if x.polarity > 0.3 else -1


# Label low polarity tweets as negative.
@labeling_function(pre=[textblob_polarity])
def polarity_negative(x):
    return 0 if x.polarity < -0.25 else -1


# Similar to polarity_negative, but with higher coverage and lower precision.
@labeling_function(pre=[textblob_polarity])
def polarity_negative_2(x):
    return 0 if x.polarity <= 0.3 else -1


# %% [markdown]
# ### Applying labeling functions to the training set

# %% {"tags": ["md-exclude-output"]}
text_lfs = [polarity_positive, polarity_negative, polarity_negative_2]
lfs = text_lfs + worker_lfs

applier = PandasLFApplier(lfs)
L_train = applier.apply(df_train)
L_dev = applier.apply(df_dev)

# %%
LFAnalysis(L_dev, lfs).lf_summary(Y_dev).head()

# %% [markdown]
# Using the text-based LFs, we've expanded coverage on both our training set
# and dev set to 100%.
# We'll now take these noisy and conflicting labels, and use the LabelModel
# to denoise and combine them.

# %%
print(f"Training set coverage: {100 * LFAnalysis(L_train).label_coverage(): 0.1f}%")
print(f"Dev set coverage: {100 * LFAnalysis(L_dev).label_coverage(): 0.1f}%")

# %% [markdown]
# ## Train LabelModel And Generate Probabilistic Labels

# %% {"tags": ["md-exclude-output"]}
from snorkel.labeling import LabelModel

# Train LabelModel.
label_model = LabelModel(cardinality=2, verbose=True)
label_model.fit(L_train, n_epochs=100, seed=123, log_freq=20, l2=0.1, lr=0.01)

# %% [markdown]
# As a spot-check for the quality of our LabelModel, we'll score it on the dev set.

# %%
from snorkel.analysis import metric_score

preds_dev = label_model.predict(L_dev)

acc = metric_score(Y_dev, preds_dev, probs=None, metric="accuracy")
print(f"LabelModel Accuracy: {acc:.3f}")

# %% [markdown]
# We see that we get very high accuracy on the development set.
# This is due to the abundance of high quality crowdworker labels.
# **Since we don't have these high quality crowdsourcing labels for the
# test set or new incoming data points, we can't use the LabelModel reliably
# at inference time.**
# In order to run inference on new incoming data points, we need to train a
# discriminative model over the tweets themselves.
# Let's generate a set of labels for that training set.

# %%
preds_train = label_model.predict(L_train)

# %% [markdown]
# ## Use Soft Labels to Train End Model

# %% [markdown]
# ### Getting features from BERT
# Since we have very limited training data, we cannot train a complex model like an LSTM with a lot of parameters.
# Instead, we use a pre-trained model, [BERT](https://github.com/google-research/bert), to generate embeddings for each our tweets, and treat the embedding values as features.
# This may take 5-10 minutes on a CPU, as the BERT model is very large.

# %% {"tags": ["md-exclude-output"]}
import numpy as np
import torch
from pytorch_transformers import BertModel, BertTokenizer

model = BertModel.from_pretrained("bert-base-uncased")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")


def encode_text(text):
    input_ids = torch.tensor([tokenizer.encode(text)])
    return model(input_ids)[0].mean(1)[0].detach().numpy()


X_train = np.array(list(df_train.tweet_text.apply(encode_text).values))
X_test = np.array(list(df_test.tweet_text.apply(encode_text).values))

# %% [markdown]
# ### Model on labels
# Now, we train a simple logistic regression model on the BERT features, using labels
# obtained from our LabelModel.

# %% {"tags": ["md-exclude-output"]}
from sklearn.linear_model import LogisticRegression

sklearn_model = LogisticRegression(solver="liblinear")
sklearn_model.fit(X_train, preds_train)

# %%
print(f"Accuracy of trained model: {sklearn_model.score(X_test, Y_test)}")

# %% [markdown]
# We now have a trained model that can be applied to future data points without requiring crowdsourced labels, and with accuracy not much lower than the `LabelModel` that _does_ have access to crowdsourced labels!

# %% [markdown]
# ## Summary
#
# In this tutorial, we accomplished the following:
# * We demonstrated how to combine crowdsourced labels with other programmatic LFs to improve coverage.
# * We used the `LabelModel` to combine inputs from crowdworkers and other LFs to generate high quality probabilistic labels.
# * We used our labels to train a classifier for making predictions on new, unseen data points.

# %% [markdown]
# # Crowdsourcing Tutorial

# %% [markdown]
# In this tutorial, we'll provide a simple walkthrough of how to use Snorkel in conjunction with crowdsourcing to create a training set for a sentiment analysis task.
# We already have crowdsourced labels for about half of the training dataset.
# The crowdsourced labels are fairly accurate, but do not cover the entire training dataset, nor are they available for the test set or during inference.
# To make up for their lack of training set coverage, we combine crowdsourced labels with heuristic labeling functions to increase the number of training labels we have.
# Like most Snorkel labeling pipelines, we'll use the denoised labels to train a deep learning
# model which can be applied to new, unseen data to automatically make predictions.

# %% [markdown]
# ## Dataset Details

# %% [markdown]
# In this tutorial, we'll use the [Weather Sentiment](https://data.world/crowdflower/weather-sentiment) dataset from Figure Eight.
# Our goal is to train a classifier that can label new tweets as expressing either a positive or negative sentiment.
#
# Crowdworkers were asked to label the sentiment of a particular tweet relating to the weather.
# The catch is that 20 crowdworkers graded each tweet, and in many cases crowdworkers assigned conflicting sentiment labels to the same tweet.
# This is a common issue when dealing with crowdsourced labeling workloads.
#
# Label options were positive, negative, or one of three other options saying they weren't sure if it was positive or negative; we use only the positive/negative labels.
# We've also altered the dataset to reflect a realistic crowdsourcing pipeline where only a subset of our available training set has received crowd labels.
#
# We will treat each crowdworker's labels as coming from a single labeling function (LF).
# This will allow us to learn a weight for how much to trust the labels from each crowdworker.
# We will also write a few heuristic labeling functions to cover the data points without crowd labels.
# Snorkel's ability to build high-quality datasets from multiple noisy labeling signals makes it an ideal framework to approach this problem.

# %% [markdown]
# ## Loading Crowdsourcing Dataset

# %% [markdown]
# We start by loading our data which has 287 data points in total.
# We take 50 for our development set and 50 for our test set.
# The remaining 187 data points form our training set.
# Since the dataset is already small, we skip using a validation set.
# Note that this very small dataset is primarily used for demonstration purposes here.
# In a real setting, we would expect to have access to many more unlabeled tweets, which could help us to train a higher quality model.

# %% {"tags": ["md-exclude"]}
import os

# Make sure we're in the right directory
if os.path.basename(os.getcwd()) == "snorkel-tutorials":
    os.chdir("crowdsourcing")

# %%
from data import load_data

crowd_labels, df_train, df_dev, df_test = load_data()
Y_dev = df_dev.sentiment.values
Y_test = df_test.sentiment.values

# %% [markdown] {"tags": ["md-exclude"]}
# First, let's take a look at our development set to get a sense of what the tweets look like.
# We use the following label convention: 0 = Negative, 1 = Positive.

# %% {"tags": ["md-exclude"]}
import pandas as pd

# Don't truncate text fields in the display
pd.set_option("display.max_colwidth", 0)

df_dev.head()

# %% [markdown] {"tags": ["md-exclude"]}
# Now let's take a look at the crowd labels.
# We'll convert these into labeling functions.

# %% {"tags": ["md-exclude"]}
crowd_labels.head()

# %% [markdown]
# ## Writing Labeling Functions
# Each crowdworker can be thought of as a single labeling function,
# as each worker labels a subset of data points,
# and may have errors or conflicting labels with other workers / labeling functions.
# So we create one labeling function per worker.
# We'll simply return the label the worker submitted for a given tweet, and abstain
# if they didn't submit a label for it.

# %% [markdown]
# ### Crowdworker labeling functions

# %%
labels_by_annotator = crowd_labels.groupby("worker_id")
worker_dicts = {}
for worker_id in labels_by_annotator.groups:
    worker_df = labels_by_annotator.get_group(worker_id)[["label"]]
    worker_dicts[worker_id] = dict(zip(worker_df.index, worker_df.label))

print("Number of workers:", len(worker_dicts))

# %%
from snorkel.labeling import LabelingFunction

ABSTAIN = -1


def worker_lf(x, worker_dict):
    return worker_dict.get(x.tweet_id, ABSTAIN)


def make_worker_lf(worker_id):
    worker_dict = worker_dicts[worker_id]
    name = f"worker_{worker_id}"
    return LabelingFunction(name, f=worker_lf, resources={"worker_dict": worker_dict})


worker_lfs = [make_worker_lf(worker_id) for worker_id in worker_dicts]

# %% [markdown]
# Let's take a quick look at how well they do on the development set.

# %% {"tags": ["md-exclude-output"]}
from snorkel.labeling import PandasLFApplier

applier = PandasLFApplier(worker_lfs)
L_train = applier.apply(df_train)
L_dev = applier.apply(df_dev)

# %% [markdown]
# Note that because our dev set is so small and our LFs are relatively sparse, many LFs will appear to have zero coverage.
# Fortunately, our label model learns weights for LFs based on their outputs on the training set, which is generally much larger.

# %%
from snorkel.labeling import LFAnalysis

LFAnalysis(L_dev, worker_lfs).lf_summary(Y_dev).sample(5)

# %% [markdown]
# So the crowd labels in general are quite good! But how much of our dev and training
# sets do they cover?

# %%
print(f"Training set coverage: {100 * LFAnalysis(L_train).label_coverage(): 0.1f}%")
print(f"Dev set coverage: {100 * LFAnalysis(L_dev).label_coverage(): 0.1f}%")

# %% [markdown]
# ### Additional labeling functions
#
# To improve coverage of the training set, we can mix the crowdworker labeling functions with labeling
# functions of other types.
# For example, we can use [TextBlob](https://textblob.readthedocs.io/en/dev/index.html), a tool that provides a pretrained sentiment analyzer. We run TextBlob on our tweets and create some simple LFs that threshold its polarity score, similar to what we did in the spam_tutorial.

# %%
from snorkel.labeling import labeling_function
from snorkel.preprocess import preprocessor
from textblob import TextBlob


@preprocessor(memoize=True)
def textblob_polarity(x):
    scores = TextBlob(x.tweet_text)
    x.polarity = scores.polarity
    return x


# Label high polarity tweets as positive.
@labeling_function(pre=[textblob_polarity])
def polarity_positive(x):
    return 1 if x.polarity > 0.3 else -1


# Label low polarity tweets as negative.
@labeling_function(pre=[textblob_polarity])
def polarity_negative(x):
    return 0 if x.polarity < -0.25 else -1


# Similar to polarity_negative, but with higher coverage and lower precision.
@labeling_function(pre=[textblob_polarity])
def polarity_negative_2(x):
    return 0 if x.polarity <= 0.3 else -1


# %% [markdown]
# ### Applying labeling functions to the training set

# %% {"tags": ["md-exclude-output"]}
text_lfs = [polarity_positive, polarity_negative, polarity_negative_2]
lfs = text_lfs + worker_lfs

applier = PandasLFApplier(lfs)
L_train = applier.apply(df_train)
L_dev = applier.apply(df_dev)

# %%
LFAnalysis(L_dev, lfs).lf_summary(Y_dev).head()

# %% [markdown]
# Using the text-based LFs, we've expanded coverage on both our training set
# and dev set to 100%.
# We'll now take these noisy and conflicting labels, and use the LabelModel
# to denoise and combine them.

# %%
print(f"Training set coverage: {100 * LFAnalysis(L_train).label_coverage(): 0.1f}%")
print(f"Dev set coverage: {100 * LFAnalysis(L_dev).label_coverage(): 0.1f}%")

# %% [markdown]
# ## Train LabelModel And Generate Probabilistic Labels

# %% {"tags": ["md-exclude-output"]}
from snorkel.labeling import LabelModel

# Train LabelModel.
label_model = LabelModel(cardinality=2, verbose=True)
label_model.fit(L_train, n_epochs=100, seed=123, log_freq=20, l2=0.1, lr=0.01)

# %% [markdown]
# As a spot-check for the quality of our LabelModel, we'll score it on the dev set.

# %%
from snorkel.analysis import metric_score

preds_dev = label_model.predict(L_dev)

acc = metric_score(Y_dev, preds_dev, probs=None, metric="accuracy")
print(f"LabelModel Accuracy: {acc:.3f}")

# %% [markdown]
# We see that we get very high accuracy on the development set.
# This is due to the abundance of high quality crowdworker labels.
# **Since we don't have these high quality crowdsourcing labels for the
# test set or new incoming data points, we can't use the LabelModel reliably
# at inference time.**
# In order to run inference on new incoming data points, we need to train a
# discriminative model over the tweets themselves.
# Let's generate a set of labels for that training set.

# %%
preds_train = label_model.predict(L_train)

# %% [markdown]
# ## Use Soft Labels to Train End Model

# %% [markdown]
# ### Getting features from BERT
# Since we have very limited training data, we cannot train a complex model like an LSTM with a lot of parameters.
# Instead, we use a pre-trained model, [BERT](https://github.com/google-research/bert), to generate embeddings for each our tweets, and treat the embedding values as features.
# This may take 5-10 minutes on a CPU, as the BERT model is very large.

# %% {"tags": ["md-exclude-output"]}
import numpy as np
import torch
from pytorch_transformers import BertModel, BertTokenizer

model = BertModel.from_pretrained("bert-base-uncased")
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")


def encode_text(text):
    input_ids = torch.tensor([tokenizer.encode(text)])
    return model(input_ids)[0].mean(1)[0].detach().numpy()


X_train = np.array(list(df_train.tweet_text.apply(encode_text).values))
X_test = np.array(list(df_test.tweet_text.apply(encode_text).values))

# %% [markdown]
# ### Model on labels
# Now, we train a simple logistic regression model on the BERT features, using labels
# obtained from our LabelModel.

# %% {"tags": ["md-exclude-output"]}
from sklearn.linear_model import LogisticRegression

sklearn_model = LogisticRegression(solver="liblinear")
sklearn_model.fit(X_train, preds_train)

# %%
print(f"Accuracy of trained model: {sklearn_model.score(X_test, Y_test)}")

# %% [markdown]
# We now have a trained model that can be applied to future data points without requiring crowdsourced labels, and with accuracy not much lower than the `LabelModel` that _does_ have access to crowdsourced labels!

# %% [markdown]
# ## Summary
#
# In this tutorial, we accomplished the following:
# * We demonstrated how to combine crowdsourced labels with other programmatic LFs to improve coverage.
# * We used the `LabelModel` to combine inputs from crowdworkers and other LFs to generate high quality probabilistic labels.
# * We used our labels to train a classifier for making predictions on new, unseen data points.

# %% [markdown] # # Crowdsourcing Tutorial # %% [markdown] # In this tutorial, we'll provide a simple walkthrough of how to use Snorkel in conjunction with crowdsourcing to create a training set for a sentiment analysis task. # We already have crowdsourced labels for about half of the training dataset. # The crowdsourced labels are fairly accurate, but do not cover the entire training dataset, nor are they available for the test set or during inference. # To make up for their lack of training set coverage, we combine crowdsourced labels with heuristic labeling functions to increase the number of training labels we have. # Like most Snorkel labeling pipelines, we'll use the denoised labels to train a deep learning # model which can be applied to new, unseen data to automatically make predictions. # %% [markdown] # ## Dataset Details # %% [markdown] # In this tutorial, we'll use the [Weather Sentiment](https://data.world/crowdflower/weather-sentiment) dataset from Figure Eight. # Our goal is to train a classifier that can label new tweets as expressing either a positive or negative sentiment. # # Crowdworkers were asked to label the sentiment of a particular tweet relating to the weather. # The catch is that 20 crowdworkers graded each tweet, and in many cases crowdworkers assigned conflicting sentiment labels to the same tweet. # This is a common issue when dealing with crowdsourced labeling workloads. # # Label options were positive, negative, or one of three other options saying they weren't sure if it was positive or negative; we use only the positive/negative labels. # We've also altered the dataset to reflect a realistic crowdsourcing pipeline where only a subset of our available training set has received crowd labels. # # We will treat each crowdworker's labels as coming from a single labeling function (LF). # This will allow us to learn a weight for how much to trust the labels from each crowdworker. # We will also write a few heuristic labeling functions to cover the data points without crowd labels. # Snorkel's ability to build high-quality datasets from multiple noisy labeling signals makes it an ideal framework to approach this problem. # %% [markdown] # ## Loading Crowdsourcing Dataset # %% [markdown] # We start by loading our data which has 287 data points in total. # We take 50 for our development set and 50 for our test set. # The remaining 187 data points form our training set. # Since the dataset is already small, we skip using a validation set. # Note that this very small dataset is primarily used for demonstration purposes here. # In a real setting, we would expect to have access to many more unlabeled tweets, which could help us to train a higher quality model. # %% {"tags": ["md-exclude"]} # Make sure we're in the right directory # %% # %% [markdown] {"tags": ["md-exclude"]} # First, let's take a look at our development set to get a sense of what the tweets look like. # We use the following label convention: 0 = Negative, 1 = Positive. # %% {"tags": ["md-exclude"]} # Don't truncate text fields in the display # %% [markdown] {"tags": ["md-exclude"]} # Now let's take a look at the crowd labels. # We'll convert these into labeling functions. # %% {"tags": ["md-exclude"]} # %% [markdown] # ## Writing Labeling Functions # Each crowdworker can be thought of as a single labeling function, # as each worker labels a subset of data points, # and may have errors or conflicting labels with other workers / labeling functions. # So we create one labeling function per worker. # We'll simply return the label the worker submitted for a given tweet, and abstain # if they didn't submit a label for it. # %% [markdown] # ### Crowdworker labeling functions # %% # %% # %% [markdown] # Let's take a quick look at how well they do on the development set. # %% {"tags": ["md-exclude-output"]} # %% [markdown] # Note that because our dev set is so small and our LFs are relatively sparse, many LFs will appear to have zero coverage. # Fortunately, our label model learns weights for LFs based on their outputs on the training set, which is generally much larger. # %% # %% [markdown] # So the crowd labels in general are quite good! But how much of our dev and training # sets do they cover? # %% # %% [markdown] # ### Additional labeling functions # # To improve coverage of the training set, we can mix the crowdworker labeling functions with labeling # functions of other types. # For example, we can use [TextBlob](https://textblob.readthedocs.io/en/dev/index.html), a tool that provides a pretrained sentiment analyzer. We run TextBlob on our tweets and create some simple LFs that threshold its polarity score, similar to what we did in the spam_tutorial. # %% # Label high polarity tweets as positive. # Label low polarity tweets as negative. # Similar to polarity_negative, but with higher coverage and lower precision. # %% [markdown] # ### Applying labeling functions to the training set # %% {"tags": ["md-exclude-output"]} # %% # %% [markdown] # Using the text-based LFs, we've expanded coverage on both our training set # and dev set to 100%. # We'll now take these noisy and conflicting labels, and use the LabelModel # to denoise and combine them. # %% # %% [markdown] # ## Train LabelModel And Generate Probabilistic Labels # %% {"tags": ["md-exclude-output"]} # Train LabelModel. # %% [markdown] # As a spot-check for the quality of our LabelModel, we'll score it on the dev set. # %% # %% [markdown] # We see that we get very high accuracy on the development set. # This is due to the abundance of high quality crowdworker labels. # **Since we don't have these high quality crowdsourcing labels for the # test set or new incoming data points, we can't use the LabelModel reliably # at inference time.** # In order to run inference on new incoming data points, we need to train a # discriminative model over the tweets themselves. # Let's generate a set of labels for that training set. # %% # %% [markdown] # ## Use Soft Labels to Train End Model # %% [markdown] # ### Getting features from BERT # Since we have very limited training data, we cannot train a complex model like an LSTM with a lot of parameters. # Instead, we use a pre-trained model, [BERT](https://github.com/google-research/bert), to generate embeddings for each our tweets, and treat the embedding values as features. # This may take 5-10 minutes on a CPU, as the BERT model is very large. # %% {"tags": ["md-exclude-output"]} # %% [markdown] # ### Model on labels # Now, we train a simple logistic regression model on the BERT features, using labels # obtained from our LabelModel. # %% {"tags": ["md-exclude-output"]} # %% # %% [markdown] # We now have a trained model that can be applied to future data points without requiring crowdsourced labels, and with accuracy not much lower than the `LabelModel` that _does_ have access to crowdsourced labels! # %% [markdown] # ## Summary # # In this tutorial, we accomplished the following: # * We demonstrated how to combine crowdsourced labels with other programmatic LFs to improve coverage. # * We used the `LabelModel` to combine inputs from crowdworkers and other LFs to generate high quality probabilistic labels. # * We used our labels to train a classifier for making predictions on new, unseen data points.