63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
from pathlib import Path
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from sklearn import svm
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from sklearn.externals import joblib
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import requests
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import glob
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import json
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import numpy as np
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from .strat_utils import write_json
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from ..util import Context, Prediction
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def write_data(time):
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write_json("https://portal.opendata.dk/api/3/action/datastore_search?resource_id=2a82a145-0195-4081-a13c-b0e587e9b89c", "parking_aarhus", time)
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def load_data():
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X = []
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Y = []
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for filename in glob.glob("parking_aarhus*"):
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p_class = '2235' in filename
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with open(filename) as file:
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data = json.load(file)
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records = data['result']['records']
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frequencies = [house['vehicleCount'] / house['totalSpaces'] for house in records]
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X.append(frequencies)
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Y.append(int(p_class))
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return np.array(X), np.array(Y)
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def train():
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X, Y = load_data()
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classifier = svm.SVC(gamma=0.01, probability=True)
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classifier.fit(X, Y)
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joblib.dump(classifier, "nightness_classifier.pkl")
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def predict(X):
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classifier = joblib.load(str(Path(__file__).parent.joinpath("nightness_classifier.pkl")))
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prob = classifier.predict_proba(np.array(X).reshape(1, -1))
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return prob[0, 1]
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def perform_svm_pred(context: Context) -> Prediction:
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"""
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An SVM trained on two data points, which is capable of guessing 0.5 no matter what.
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"""
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p = Prediction()
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p.weight = 0.5
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data = requests.get('https://portal.opendata.dk/api/3/action/datastore_search?resource_id=2a82a145-0195-4081-a13c-b0e587e9b89c')
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records = data.json()['result']['records']
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X = [house['vehicleCount'] / house['totalSpaces'] for house in records]
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X = [min(x, 1) for x in X]
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p.reasons.append("We only have two data points")
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p.reasons.append("Our only two data points have 11 dimensions")
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p.reasons.append("We are using a SVM. Apparently that's a poor idea.")
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p.probability = float(predict(X))
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return p
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