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koaning/biased-hard.csv

Last active Nov 29, 2019
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import numpy as np
import pandas as pd
import matplotlib.pylab as plt
from sklearn.pipeline import make_pipeline
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import recall_score, precision_score, accuracy_score
from sklego.preprocessing import ColumnSelector
from prettytable import PrettyTable
class Experiment():
def __init__(self, estimator, biased_data, actual_data, seed=42):
self.estimator = estimator
self.x_train = biased_data[['x1', 'x2']].values
self.y_train = biased_data['y'].values
self.seed = seed
self.x_actual = actual_data[['x1', 'x2']].values
self.y_actual = actual_data['y'].values
self.round = 0
self.logs = None
self.estimator.fit(self.x_train, self.y_train)
def retrain(self):
learned = self.logs.loc[lambda d: d['pred'] == 1]
X = np.concatenate([self.x_train, learned[['x1', 'x2']].values])
y = np.concatenate([self.y_train, learned['true'].values])
self.estimator.fit(X, y)
def scores(self):
learned = self.logs.loc[lambda d: d['pred'] == 1]
X = np.concatenate([self.x_train, learned[['x1', 'x2']].values])
y = np.concatenate([self.y_train, learned['true'].values])
print(f"measured scores on seen data; acc={accuracy_score(y, self.estimator.predict(X))}")
print(f"measured scores on seen data; precision={precision_score(y, self.estimator.predict(X))}")
print(f"measured scores on seen data; recall={recall_score(y, self.estimator.predict(X))}")
return self.estimator.score(X, y)
def step(self, n_proposal, n_select, method):
self.round += 1
subset_y, self.y_actual = self.y_actual[:n_proposal], self.y_actual[n_proposal:]
subset_x, self.x_actual = self.x_actual[:n_proposal, :], self.x_actual[n_proposal:, :]
chosen_idx, not_chosen_idx, proba = method(self, subset_x, n_select)
df1 = pd.DataFrame({
'x1': subset_x[chosen_idx][:, 0],
'x2': subset_x[chosen_idx][:, 1],
'pred': [1 for i in subset_y[chosen_idx]],
'proba': proba[chosen_idx],
'true': subset_y[chosen_idx],
'round': [self.round for i in subset_y[chosen_idx]],
})
df2 = pd.DataFrame({
'x1': subset_x[not_chosen_idx][:, 0],
'x2': subset_x[not_chosen_idx][:, 1],
'pred': [0 for i in subset_y[not_chosen_idx]],
'proba': proba[not_chosen_idx],
'true': subset_y[not_chosen_idx],
'round': [self.round for i in subset_y[not_chosen_idx]],
})
logz = df1.append(df2)
self.logs = logz if self.logs is None else self.logs.append(logz)
self.retrain()
def greedy(experiment, x, n):
"""
est - sklearn estimator going in
x - data points to predict
n - the number of points to return
"""
preds = experiment.estimator.predict_proba(x)
order = np.lexsort((np.arange(len(preds)), -preds[:, 1]))
return order[:n], order[n:], preds[:, 1]
def sploit(experiment, x, n):
"""
everybody who scores higher than 80% is equal
"""
preds = experiment.estimator.predict_proba(x)
p = np.array(preds[:, 1]) + 0.001
best_candidate = np.argmax(p)
p[best_candidate] = 1
order = np.random.choice(range(len(p)), p=p/p.sum(), replace=False, size=len(p))
return order[:n], order[n:], preds[:, 1]
def plot_experiment(biased_start, other_data, size=10, selection=1, method=greedy):
experiment = Experiment(
estimator=KNeighborsClassifier(n_neighbors=20),
biased_data=biased_start,
actual_data=other_data
)
df = pd.concat([biased_start, other_data])
min_x1, max_x1 = df['x1'].min(), df['x1'].max()
min_x2, max_x2 = df['x2'].min(), df['x2'].max()
while True:
try:
experiment.step(size, selection, method)
except ValueError:
break # this just means we've exhausted the dataset
print(f"method={method.__name__} size={size} selection={selection}")
experiment.scores()
print(f"measured scores on real data; acc={accuracy_score(experiment.logs['true'], experiment.logs['proba'] > 0.5)}")
print(f"measured scores on real data; precision={precision_score(experiment.logs['true'], experiment.logs['proba'] > 0.5)}")
print(f"measured scores on real data; recall={recall_score(experiment.logs['true'], experiment.logs['proba'] > 0.5)}")
plt.figure(figsize=(16, 4))
plt.subplot(141)
plt.scatter(df['x1'], df['x2'], c=df['y'])
plt.title("this is the original dataset")
plt.xlim(min_x1, max_x1)
plt.ylim(min_x2, max_x2);
plt.subplot(142)
plt.scatter(biased_start['x1'], biased_start['x2'], c=biased_start['y'])
plt.title("this is the biased start")
plt.xlim(min_x1, max_x1)
plt.ylim(min_x2, max_x2);
plt.subplot(143)
plt.scatter(experiment.logs['x1'], experiment.logs['x2'], c=experiment.logs['proba']);
plt.xlim(min_x1, max_x1)
plt.ylim(min_x2, max_x2)
plt.title("this is what the model believes");
plt.subplot(144)
plt.scatter(experiment.logs['x1'], experiment.logs['x2'], c=experiment.logs['pred']);
plt.xlim(min_x1, max_x1)
plt.ylim(min_x2, max_x2)
plt.title("these are the selected members");
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