def validate(training_settings_pickle, test_data_file, output_dir):
# Parse data
loader = BiocLoader()
data = loader.parse(test_data_file)
# Run validation
pipeline = Pipeline()
result = pipeline.validate(data, training_settings_pickle)
#Save results
output_file = tempfile.mkstemp(prefix=basename(test_data_file),
suffix=".xml")[1]
loader.dump(result, filename=output_file)
def main(argv):
# construct our pipeline list reading from command line args
# still need to figure out best way to pass parameters on command
# line
global verbose
global norm
split = None
transforms = []
for arg in argv[0].split(","):
if arg == "toke":
transforms.append(Tokenizer())
elif arg == "stem":
transforms.append(Stemmer())
elif arg == "stem-porter":
transforms.append(Stemmer(mode='Porter'))
elif arg == "stem-lancaster":
transforms.append(Stemmer(mode='Lancaster'))
elif arg == "stem-lemmatize":
transforms.append(Stemmer(mode='Lemmatize'))
elif arg == "vect":
transforms.append(Vectorizer())
elif arg == "vect-tfidf":
transforms.append(Vectorizer(mode='TFIDF'))
elif arg == "vect-count":
transforms.append(Vectorizer(mode='Count'))
elif arg == "vect-lda-2":
transforms.append(Vectorizer(mode='LDA', ldaSplits=2))
elif arg == "vect-lda-10":
transforms.append(Vectorizer(mode='LDA', ldaSplits=10))
elif arg == "vect-lda-25":
transforms.append(Vectorizer(mode='LDA', ldaSplits=25))
elif arg == "vect-lda-50":
transforms.append(Vectorizer(mode='LDA', ldaSplits=50))
elif arg == "vect-lda-150":
transforms.append(Vectorizer(mode='LDA', ldaSplits=150))
elif arg == "vect-lda-500":
transforms.append(Vectorizer(mode='LDA', ldaSplits=500))
elif arg == "svm":
transforms.append(Model('svm'))
elif arg == "nb":
transforms.append(Model('nb'))
elif arg == "lr":
transforms.append(Model('lr'))
elif arg == "nn":
transforms.append(
Model('nn', inputDim=10000)
) #Configured for Vectorizer with vectors limited to 1000
elif arg == "norm":
norm = True
elif arg == "no-verb":
verbose = False
elif arg == "split-sentences":
split = "sentences"
elif arg == "nn-optim":
# Memory optimized neural network.
transforms.append(
OptimNN(vecMode='TFIDF', epochs=2, batchSize=2048))
else:
raise Exception(f"Invalid transformer {arg}")
pipe = Pipeline(transforms, norm=norm)
# read our data (hardcoded for now)
df0 = pd.read_pickle(
"./data/democrat_comments.pkl") #.sample(frac = 0.05) # DEBUG ONLY
df1 = pd.read_pickle(
"./data/republican_comments.pkl") #.sample(frac = 0.05) # DEBUG ONLY
if (split is not None):
if (verbose):
print('Splitting Democrat comments')
df0 = splitRows(df0, mode=split, verbose=verbose)
if (verbose):
print('Splitting Republican comments')
df1 = splitRows(df1, mode=split, verbose=verbose)
label0 = df0.subreddit.iloc[0]
label1 = df1.subreddit.iloc[0]
# concatenate and clean our data
X = pd.concat([df0.body, df1.body], ignore_index=True)
y = pd.concat([df0.subreddit, df1.subreddit],
ignore_index=True).replace(to_replace=[label0, label1],
value=[0, 1])
# split into training and test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=42)
if (verbose):
print('Applying Transforms and Training Model')
print('Train Data:', train_path)
print('Test Data:', test_path)
print('Transforms:', argv[0])
# fit our data
pipe.fit_transform(X_train, y_train)
# do the prediction
y_pred = pipe.predict(X_test)
results = pipe.validate(y_pred, y_test, True, True)
# get most suprising misclassifications for class 0
print("Most suprising texts misclassified as class 0")
idx_list = heapq.nlargest(5, results[2][0], key=lambda x: x[1])
for i, (idx, prob) in enumerate(idx_list):
print(f"{i}) probability class 1 = {prob}\n{X_test[idx]}, \n")
# get most suprising misclassifications for class 1
print("Most suprising texts misclassified as class 1")
idx_list = heapq.nlargest(5, results[2][1], key=lambda x: x[1])
for i, (idx, prob) in enumerate(idx_list):
print(f"{i}) probability class 0 = {prob}\n{X_test[idx]}\n")
