def process(self, use_chi2=True, chi2_param=5000,nb_max=1000):
if not self.silent:
print_color("Frodo is processing data.",COLORS.GREEN)
training_data = np.array(self.training_data[:nb_max])
interview_text = training_data[:,1]
Y = training_data[:,2].astype(int)
df_train = np.array([interview_text, Y]).T
# training to be done
self.wv = WordVectorizer(df_train, contains_prediction=True, use_chi2=use_chi2, chi2_param=chi2_param)
X = self.wv.convert_to_word_vector(df_train[:,0])
X[X > 0] = 1
# Y = df_train[:,1]
# assert len(X) == len(Y), str(X.shape)+' is not equal to '+str(Y.shape)
# print(df_train)
# print(X.shape, Y.shape)
# print(df_train.shape)
# internalpurposes
self.X_internal = X
self.Y_internal = Y
self.df_train_internal = df_train
self.interview_text_internal = interview_text
try:
self.predictor = multiple_naive_bayes(X,Y)
except Exception as e:
print(str(e))
#don't remove this:
self.has_processed=True
def run_nb(train_size=2000,
ngram_range=(1, 1),
smoothing=1,
learn_code='0000',
kbest=None):
df_complete = pd.read_csv('./clean/ml_dataset_train-' + learn_code +
'.csv',
index_col=0)
n = len(df_complete)
train_split = 0.8
# subset of the training set for performance purposes
subset_indicies = np.random.choice(
[x for x in range(0, int(n * train_split))], size=train_size)
df_train = df_complete.iloc[subset_indicies]
# subset of testing set for performance purposes
subset_indicies = np.random.choice(
[x for x in range(int(n * train_split), n)], size=2000)
df_test = df_complete.iloc[subset_indicies]
# create a word vector using our training set
if kbest:
wv = WordVectorizer(df_train.values,
contains_prediction=True,
ngram_range=ngram_range,
use_chi2=True,
chi2_param=kbest)
else:
wv = WordVectorizer(df_train.values,
contains_prediction=True,
ngram_range=ngram_range)
# make improvement here, convert to indicator
X_train = wv.convert_to_word_vector(df_train.values.T[0])
X_train[X_train > 0] = 1
Y_train = df_train.values.T[1]
assert len(X_train) == len(Y_train)
# train the model
predictor = multiple_naive_bayes(X_train, Y_train)
# conver to indicator
X_test = wv.convert_to_word_vector(df_test.values.T[0])
X_test[X_test > 0] = 1
Y_test = df_test.values.T[1]
# get predictions
predicted = predictor(X_test)
return Y_test, predicted
def run_nb(train_size=2000,ngram_range=(1,1),smoothing=1, learn_code='0000', kbest=None):
df_complete = pd.read_csv('./clean/ml_dataset_train-'+learn_code+'.csv',index_col=0)
n = len(df_complete)
train_split = 0.8
# subset of the training set for performance purposes
subset_indicies = np.random.choice([x for x in range(0, int(n*train_split)) ],size=train_size)
df_train = df_complete.iloc[subset_indicies]
# subset of testing set for performance purposes
subset_indicies = np.random.choice([x for x in range(int(n*train_split), n) ],size=2000)
df_test = df_complete.iloc[subset_indicies]
# create a word vector using our training set
if kbest:
wv = WordVectorizer(df_train.values, contains_prediction=True, ngram_range=ngram_range, use_chi2=True, chi2_param=kbest)
else:
wv = WordVectorizer(df_train.values, contains_prediction=True, ngram_range=ngram_range)
# make improvement here, convert to indicator
X_train = wv.convert_to_word_vector(df_train.values.T[0])
X_train[X_train > 0] = 1
Y_train = df_train.values.T[1]
assert len(X_train) == len(Y_train)
# train the model
predictor = multiple_naive_bayes(X_train,Y_train)
# conver to indicator
X_test = wv.convert_to_word_vector(df_test.values.T[0])
X_test[X_test > 0] = 1
Y_test = df_test.values.T[1]
# get predictions
predicted = predictor(X_test)
return Y_test, predicted
