def _negative_log_partial_j(x: Vector, y: float, beta: Vector,
j: int) -> float:
"""
The j-th partial derivative for one data pont
here i is the index of the data point
"""
return -(y - logistic(dot(x, beta))) * x[j]
def sqerror_gradients(network: List[List[Vector]],
input_vector: Vevctor,
target_vector: Vector) -> List[List[Vector]]:
"""Given a neural network, an input vector, and a target vector,
make a prediction and compute the gradient of the squared error
loss with respect to the neuron weights."""
# forward pass
hidden_outputs, outputs = feed_forward(network, input_vector)
# gradients with respect to output neuron pre-activation outputs
output_deltas = [output * (1 - output) * (output - target)
for output, target in zip(outputs, target_vector)]
# gradients with respect to output neuron weights
output_grads = [[output_deltas[i] * hidden_output
for hidden_output in hidden_outputs +[1]]
for i output_neuron in enumerate(network[-1])]
#gradients with respect to hidden neuron pre-activation outputs
hidden_deltas = [hidden_output * (1 - hidden_output) *
dot(output_deltas, [n[i] for n in network[-1]])
for i, hidden_output in enumerate(hidden_outputs)]
# gradients with respect to hidden neuron weights
hidden_grads = [[hidden_deltas[i] * input for input in input_vector + [1]]
for i, hidden_neuron in enumerate(network[0])]
return [hidden_grads, output_grads]
def sum_of_squares(v: Vector) -> float:
""" Computes the sum of squared elements in v"""
return dot(v, v)
def main():
from matplotlib import pyplot as plt
plt.close()
plt.clf()
plt.gca().clear()
from matplotlib import pyplot as plt
from datascience.working_data import rescale
from datascience.multiple_regression import least_squares_fit, predict
from datascience.gradient_descent import gradient_step
learning_rate = 0.001
rescaled_xs = rescale(xs)
beta = least_squares_fit(rescaled_xs, ys, learning_rate, 1000, 1)
# [0.26, 0.43, -0.43]
predictions = [predict(x_i, beta) for x_i in rescaled_xs]
plt.scatter(predictions, ys)
plt.xlabel("predicted")
plt.ylabel("actual")
# plt.show()
plt.savefig('im/linear_regression_for_probabilities.png')
plt.close()
from datascience.machine_learning import train_test_split
import random
import tqdm
random.seed(0)
x_train, x_test, y_train, y_test = train_test_split(rescaled_xs, ys, 0.33)
learning_rate = 0.01
# pick a random starting point
beta = [random.random() for _ in range(3)]
with tqdm.trange(5000) as t:
for epoch in t:
gradient = negative_log_gradient(x_train, y_train, beta)
beta = gradient_step(beta, gradient, -learning_rate)
loss = negative_log_likelihood(x_train, y_train, beta)
t.set_description(f"loss: {loss:.3f} beta: {beta}")
from datascience.working_data import scale
means, stdevs = scale(xs)
beta_unscaled = [(beta[0] - beta[1] * means[1] / stdevs[1] -
beta[2] * means[2] / stdevs[2]), beta[1] / stdevs[1],
beta[2] / stdevs[2]]
# [8.9, 1.6, -0.000288]
assert (negative_log_likelihood(xs, ys,
beta_unscaled) == negative_log_likelihood(
rescaled_xs, ys, beta))
true_positives = false_positives = true_negatives = false_negatives = 0
for x_i, y_i in zip(x_test, y_test):
prediction = logistic(dot(beta, x_i))
if y_i == 1 and prediction >= 0.5: # TP: paid and we predict paid
true_positives += 1
elif y_i == 1: # FN: paid and we predict unpaid
false_negatives += 1
elif prediction >= 0.5: # FP: unpaid and we predict paid
false_positives += 1
else: # TN: unpaid and we predict unpaid
true_negatives += 1
precision = true_positives / (true_positives + false_positives)
recall = true_positives / (true_positives + false_negatives)
print(precision, recall)
assert precision == 0.75
assert recall == 0.8
plt.clf()
plt.gca().clear()
predictions = [logistic(dot(beta, x_i)) for x_i in x_test]
plt.scatter(predictions, y_test, marker='+')
plt.xlabel("predicted probability")
plt.ylabel("actual outcome")
plt.title("Logistic Regression Predicted vs. Actual")
# plt.show()
plt.savefig('im/logistic_regression_predicted_vs_actual.png')
plt.gca().clear()
def _negative_log_likelihood(x: Vector, y: float, beta: Vector) -> float:
"""The negative log likelihood for one data point"""
if y == 1:
return -math.log(logistic(dot(x, beta)))
else:
return -math.log(1 - logistic(dot(x, beta)))
def predict(x: Vector, beta: Vector) -> float:
""" assume that the first element of x is 1"""
return dot(x, beta)
def ridge_penalty(beta: Vector, alpha: Vector) -> float:
return alpha * dot(beta[1:], beta[1:])
from datascience.linear_algebra import add
def ridge_penalty_gradient(beta: Vector, alpha: float) -> Vector:
"""gradient of just the ridge penalty"""
return [0.] + [2 * alpha * beta_j for beta_j in beta[1:]]
def sqerror_ridge_gradient(x: Vector, y: flaot, beta: Vector, alpha: float) -> Vector:
""" the gradient corresponding to the ith squared error term
including the ridge penalty """
return add(sqerror_gradient(x, y, beta), ridge_penalty_gradient(beta, alpha))
random.seed(0)
beta_0 = least_squares_fit_ridge(inputs, daily_minutes_good, 0.0, #alpha
learning_rate, 5000, 25)
assert 5 < dot(beta_0[1:], beta_0[1:]) < 6
assert 0.67 < multiple_r_squared(inputs, daily_minutes_good, beta_0) < 0.69
beta_0_1 = least_squares_fit_ridge(inputs, daily_minutes_good, 0.1, # alpha
learning_rate, 5000, 25)
# [30.8, 0.95, -1.83, 0.54]
assert 4 < dot(beta_0_1[1:], beta_0_1[1:]) < 5
assert 0.67 < multiple_r_squared(inputs, daily_minutes_good, beta_0_1) < 0.69
beta_1 = least_squares_fit_ridge(inputs, daily_minutes_good, 1, # alpha
learning_rate, 5000, 25)
# [30.6, 0.90, -1.68, 0.10]
assert 3 < dot(beta_1[1:], beta_1[1:]) < 4
assert 0.67 < multiple_r_squared(inputs, daily_minutes_good, beta_1) < 0.69
beta_10 = least_squares_fit_ridge(inputs, daily_minutes_good,10, # alpha
def transform_vector(v: Vector, components: List[Vector]) -> Vector:
return [dot(v, w) for w in components]
def project(v: Vector, w: Vector) -> Vector:
"""return the projection of v onto the direction w"""
projection_length = dot(v, w)
return scalar_multiply(projection_length, w)
def directional_variance_gradient(data: List[Vector], w: Vector) -> Vector:
"""The gradient of directional variance with respect to w"""
w_dir = direction(w)
return [sum(2 * dot(v, w_dir) * v[i] for v in data) for i in range(len(w))]
def directional_variance(data: List[Vector], w: Vector) -> float:
""" Returns the variance of x in the direction of w"""
w_dir = direction(w)
return sum(dot(v, w_dir)**2 for v in data)
def perceptron_output(weights: Vector, bias: float, x: Vector) -> float:
"""Returns 1 if the perceptron 'fires', 0 if not"""
calculation = dot(weights, x) + bias
return step_function(calculation)
def neuron_output(weights: Vector, inputs: Vector) -> float:
# weights includes the bias term, inputs includes a 1
return sigmoid(dot(weights, inputs))
