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| import pickle | |
| import pandas as pd | |
| from sklearn.utils import resample | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.svm import LinearSVC | |
| import os | |
| class PokemonTypeIdentifier(): | |
| """ | |
| This class identifies the pokemon type of a user given pokemon name. | |
| """ | |
| def __init__(self): | |
| self.isModelLoaded = False | |
| self.isFileFound = False | |
| if os.path.isfile("models/tfidf.pickle") and os.path.isfile("models/model.pickle"): | |
| self.tfidf = pickle.load(open("models/tfidf.pickle","rb")) | |
| self.model = pickle.load(open("models/model.pickle","rb")) | |
| self.isModelLoaded = True | |
| if os.path.isfile('updated_pokemon.csv'): | |
| df = pd.read_csv('updated_pokemon.csv') | |
| category = list(dict(df['Type 1'].value_counts()).keys()) | |
| df_majority = df[df['Type 1'] == 'Water'] | |
| for i in range(1,len(category)): | |
| df_minority = df[df['Type 1'] == category[i]] | |
| df_minority_upsampled = resample(df_minority, | |
| replace=True, # sample with replacement | |
| n_samples=103, # to match majority class | |
| random_state=123) # reproducible results | |
| df_majority = pd.concat([df_majority, df_minority_upsampled]) | |
| encoded_labels,decoded_labels = pd.factorize(df_majority['Type 1']) | |
| self.decoded_labels = decoded_labels | |
| self.isFileFound = True | |
| if not self.isModelLoaded and self.isFileFound: | |
| self.tfidf = TfidfVectorizer(min_df=2, max_features = None, strip_accents = 'unicode', norm='l2', | |
| analyzer = 'char', token_pattern = r'\w{1,}',ngram_range=(1,5), | |
| use_idf = 1, smooth_idf = 1, sublinear_tf = 1, stop_words = 'english') | |
| features = self.tfidf.fit_transform(df_majority['Name']).toarray() | |
| encoded_labels,decoded_labels = pd.factorize(df_majority['Type 1']) | |
| self.model = LinearSVC().fit(features,encoded_labels) | |
| self.decoded_labels = decoded_labels | |
| self.isModelLoaded = True | |
| if not self.isModelLoaded or not self.isFileFound: | |
| raise AttributeError("Required File Doesn't Exist.") | |
| def predict_type(self,poke_str): | |
| """ | |
| Finds the probable Pokemon type given the user string. | |
| Input: A string, of which type is to be identified. | |
| Output: The Probable pokemon type | |
| """ | |
| return self.decoded_labels[self.model.predict(self.tfidf.transform([poke_str]))[0]] | |