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| __copyright__ = "Copyright (C) 2023 Ali Mustapha" | |
| __license__ = "GPL-3.0-or-later" | |
| import pandas as pd | |
| import numpy as np | |
| import tensorflow as tf | |
| import pickle | |
| class RegionPredictor: | |
| def __init__(self, models_directory): | |
| self.models_directory = models_directory | |
| def load_model(self, path): | |
| model = tf.keras.models.load_model(path+"bestmodel.tf") | |
| #compile and train the model | |
| model.compile( | |
| loss = tf.keras.losses.categorical_crossentropy, | |
| optimizer=tf.keras.optimizers.Adam(), | |
| metrics=['accuracy']) | |
| with open(path+'label_encoder.pkl', 'rb') as file: | |
| label_encoder = pickle.load(file) | |
| with open(path+'optimal_thresholds_f1.pkl', 'rb') as file: | |
| optF1 = pickle.load(file) | |
| with open(path+'optimal_thresholds_ROC.pkl', 'rb') as file: | |
| optROC = pickle.load(file) | |
| return model,label_encoder,optF1,optROC | |
| def model_prediction(self, dataset,model,label_encoder,optF1=None,optROC=None): | |
| input_Full_name=np.asarray(dataset['Author']).astype('str') | |
| input_offset=np.asarray(dataset['Author_Timezone']).astype('float') | |
| predictions_proba = model.predict({ | |
| "input_text": input_Full_name, | |
| "input_offset": input_offset | |
| }) | |
| # predictions = np.argmax(predictions_proba,axis=1) | |
| y_pred_F1=[] | |
| y_pred_ROC=[] | |
| if optF1 is not None: | |
| y_pred_F1 = (predictions_proba >= optF1).astype(int) | |
| y_pred_F1=np.argmax(y_pred_F1,axis=1) | |
| y_pred_F1 = label_encoder.inverse_transform(y_pred_F1) | |
| if optROC is not None: | |
| y_pred_ROC = (predictions_proba >= optROC).astype(int) | |
| y_pred_ROC=np.argmax(y_pred_ROC,axis=1) | |
| y_pred_ROC = label_encoder.inverse_transform(y_pred_ROC) | |
| return y_pred_F1,y_pred_ROC | |
| def get_region(self,dataset): | |
| dataset["Author_Timezone"]= dataset["Author_Timezone"] /60 | |
| model,label_encoder,optF1,optROC=self.load_model(self.models_directory+"/region/files/") | |
| y_pred,_=self.model_prediction(dataset,model,label_encoder,optF1,optROC) | |
| dataset["region-prediction"]=y_pred | |
| Europe=dataset[dataset["region-prediction"]=="Europe"] | |
| Africa=dataset[dataset["region-prediction"]=="Africa"] | |
| Asia=dataset[dataset["region-prediction"]=="Asia"] | |
| Americas=dataset[dataset["region-prediction"]=="Americas"] | |
| Oceania=dataset[dataset["region-prediction"]=="Oceania"] | |
| if not Europe.empty: | |
| model,label_encoder,optF1,optROC=self.load_model(self.models_directory+"/Europe/files/") | |
| y_pred,_=self.model_prediction(Europe,model,label_encoder,optF1,optROC) | |
| Europe["sub-region-prediction"]=y_pred | |
| if not Asia.empty: | |
| model,label_encoder,optF1,optROC=self.load_model(self.models_directory+"/Asia/files/") | |
| y_pred,_=self.model_prediction(Asia,model,label_encoder,optF1,optROC) | |
| Asia["sub-region-prediction"]=y_pred | |
| if not Americas.empty: | |
| model,label_encoder,optF1,optROC=self.load_model(self.models_directory+"/Americas/files/") | |
| y_pred,_=self.model_prediction(Americas,model,label_encoder,optF1,optROC) | |
| Americas["sub-region-prediction"]=y_pred | |
| if not Oceania.empty: | |
| Oceania["sub-region-prediction"]="Australia and New Zealand" | |
| if not Africa.empty: | |
| Africa["sub-region-prediction"]="Africa" | |
| data=pd.concat([Europe,Asia,Oceania,Americas,Africa]) | |
| return data | |