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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]]
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