class Predictor_cat5():
def __init__(self):
self.traits = ['OPN', 'CON', 'EXT', 'AGR', 'NEU']
self.categories = [
'OPENNESS', 'CONSCIENTIOUSNESS', 'EXTRAVERSION', 'AGREEABLENESS',
'NEUROTICISM'
]
self.Pre_cat = {
trait: cat
for (trait, cat) in zip(self.traits, self.categories)
}
self.models = {
trait: pickle.load(
open(os.getcwd() + '/model/' + trait + '_model.pkl', 'rb'))
for i, trait in enumerate(self.traits)
}
self.dp = DataPrep()
def predict(self, X, traits='All', predictions='All'):
predictions = {}
self.dp.transform(X)
if traits == 'All':
for trait in self.traits:
pkl_model = self.models[trait]
# trait_categories = pkl_model.predict(X, regression=False)
# predictions[self.Pre_cat[trait]+' '] = str(trait_categories[0])
# trait_scores = pkl_model.predict(X, regression=True).reshape(1, -1)
# predictions[self.Pre_cat[trait]+' '] = predictions[self.Pre_cat[trait]+' ']+' '+str(round(trait_scores.flatten()[0]*10))+' % '
trait_categories_probs = pkl_model.predict_proba(X)
predictions[self.Pre_cat[trait] + ' '] = str(
trait_categories_probs[:, 1][0] * 100)
return predictions
class Predictor_cat5():
def __init__(self):
"""
Loading all regression and classification models of cat5 models (model no 1 )
"""
self.traits = ['OPN', 'CON', 'EXT', 'AGR', 'NEU']
self.categories = [
'OPENNESS', 'CONSCIENTIOUSNESS', 'EXTRAVERSION', 'AGREEABLENESS',
'NEUROTICISM'
]
self.Pre_cat = {
trait: cat
for (trait, cat) in zip(self.traits, self.categories)
}
self.models = {
trait: pickle.load(open('static/' + trait + '_model.pkl', 'rb'))
for i, trait in enumerate(self.traits)
}
self.dp = DataPrep()
def predict(self, X, traits='All', predictions='All'):
"""
Takes features and returns predictions
Transforming text into vector and predicting probablity on text
"""
predictions = {}
self.dp.transform(X)
if traits == 'All':
for trait in self.traits:
pkl_model = self.models[trait]
# trait_categories = pkl_model.predict(X, regression=False)
# predictions[self.Pre_cat[trait]+' '] = str(trait_categories[0])
# trait_scores = pkl_model.predict(X, regression=True).reshape(1, -1)
# predictions[self.Pre_cat[trait]+' '] = predictions[self.Pre_cat[trait]+' ']+' '+str(round(trait_scores.flatten()[0]*10))+' % '
trait_categories_probs = pkl_model.predict_proba(X)
predictions[self.Pre_cat[trait] +
' '] = trait_categories_probs[:, 1][0] * 100
return predictions