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def test_neighbors_metrics(n_samples=20, n_features=3,
                           n_query_pts=2, n_neighbors=5):
    # Test computing the neighbors for various metrics
    # create a symmetric matrix
    V = rng.rand(n_features, n_features)
    VI = np.dot(V, V.T)

    metrics = [('euclidean', {}),
               ('manhattan', {}),
               ('minkowski', dict(p=1)),

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• Download ads_bot.py and requirements.txt.
• Type pip install -r requirements.txt to install the requirements.
• Fill out the required information in the Python file.
• Ideally, create a (free) Slack account and set up a web hook to receive notifications from the bot.
• Run the script :)
• Relax and be ready to answer incoming calls :D

saihttam

import pandas as pd
from sklearn.feature_extraction import DictVectorizer

def one_hot_dataframe(data, cols, replace=False):
    """ Takes a dataframe and a list of columns that need to be encoded.
        Returns a 3-tuple comprising the data, the vectorized data,
        and the fitted vectorizor."""
    vec = DictVectorizer(sparse=False)
    vecData = pd.DataFrame(vec.fit_transform(data[cols].T.to_dict().values()))
    vecData.columns = vec.get_feature_names()

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def plot_roc(y, probs, threshmarkers=None):
    fpr, tpr, thresh = sklearn.metrics.roc_curve(y, probs)

    plt.plot(fpr, tpr, lw=2)
    if threshmarkers is None:
        threshmarkers = np.linspace(0, 1, 11)
    for t in threshmarkers:
        k = np.abs(thresh-t).argmin()
        x = fpr[k]
        y = tpr[k]