52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
from rest_framework.decorators import api_view
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import pandas as pd
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import math
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from rest_framework.response import Response
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from sqlalchemy import create_engine
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import ras.settings
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from django.http import JsonResponse
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from .functions import isAuthorized, getBooksData, filterData
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from sklearn.linear_model import LinearRegression
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def getChallenges():
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engine = create_engine('mysql+mysqldb://' + ras.settings.DATABASES['default']['USER'] + ':' + ras.settings.DATABASES['default']['PASSWORD'] + '@' + ras.settings.DATABASES['default']['HOST'] + ':3306/' + ras.settings.DATABASES['default']['NAME'])
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df = pd.read_sql('SELECT * FROM book_challenge', engine)
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return df
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def train_model_challenges(data):
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df = pd.DataFrame(data)
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X = df.index.values.reshape(-1, 1)
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Y = df['nrofbooks']
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model = LinearRegression()
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model.fit(X, Y)
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return model
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def predict_challenge(model):
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future_index = pd.RangeIndex(start=0, stop=1, step=1)
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future_X = future_index.values.reshape(-1, 1)
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predicted_books = model.predict(future_X)
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prediction_next_year = round(predicted_books[-1])
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return prediction_next_year
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@api_view(['GET'])
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def predict_next_year(request):
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if(request.headers.get('Authorization')):
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isLoggedIn = isAuthorized(request.headers.get('Authorization'));
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if(isLoggedIn):
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data = getChallenges()
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trained_model = train_model_challenges(data)
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next_year_challenge = predict_challenge(trained_model)
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return Response(f"Volgend jaar lees ik {next_year_challenge} boeken")
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# def train_model_books_per_month():
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# def predict_books_per_month():
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# def train_model_genres():
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# def predict_genres(): |