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charts.py
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charts.py
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import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
# Learning code plots retrieved from https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html
def plot_learning_curve(
train_scores,
val_scores,
train_sizes,
title,
y_axis,
axes=None,
):
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
val_scores_mean = np.mean(val_scores, axis=1)
val_scores_std = np.std(val_scores, axis=1)
axes.set_title(title,fontsize=20)
axes.set_xlabel("Training examples",fontsize=16)
axes.set_ylabel(y_axis,fontsize=16)
axes.grid()
# axes.fill_between(
# train_sizes,
# train_scores_mean - train_scores_std,
# train_scores_mean + train_scores_std,
# alpha=0.1,
# color="r",
# )
# axes.fill_between(
# train_sizes,
# val_scores_mean - val_scores_std,
# val_scores_mean + val_scores_std,
# alpha=0.1,
# color="b",
# )
axes.plot(
train_sizes, train_scores_mean, "o-", color="r", label="Training"
)
axes.plot(
train_sizes, val_scores_mean, "o-", color="b", label="Cross-validation"
)
axes.legend(loc="best")
def plot_fit_time(
fit_times,
train_sizes,
title,
y_axis,
axes=None,
):
fit_times = fit_times*1000
fit_times_mean = np.mean(fit_times, axis=1)
fit_times_std = np.std(fit_times, axis=1)
axes.set_title(title,fontsize=20)
axes.set_xlabel("Training examples",fontsize=16)
axes.set_ylabel("Fit Time (ms)",fontsize=16)
axes.grid()
# axes.fill_between(
# train_sizes,
# fit_times_mean - fit_times_std,
# fit_times_mean + fit_times_std,
# alpha=0.1,
# color="r",
# )
axes.plot(
train_sizes, fit_times_mean, "o-", color="r"
)
def plot_predict_time(
predict_times,
train_sizes,
title,
y_axis,
axes=None,
):
predict_times = predict_times * 1000
predict_times_mean = np.mean(predict_times, axis=1)
predict_times_std = np.std(predict_times, axis=1)
axes.set_title(title,fontsize=20)
axes.set_xlabel("Training examples",fontsize=16)
axes.set_ylabel("Predict Time (ms)",fontsize=16)
axes.grid()
# axes.fill_between(
# train_sizes,
# predict_times_mean - predict_times_std,
# predict_times_mean + predict_times_std,
# alpha=0.1,
# color="r",
# )
axes.plot(
train_sizes, predict_times_mean, "o-", color="r"
)
def plot_validation_curve(
train_scores,
val_scores,
val_values,
title,
y_axis,
x_axis,
axes=None,
x_log=None
):
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
val_scores_mean = np.mean(val_scores, axis=1)
val_scores_std = np.std(val_scores, axis=1)
axes.set_title(title,fontsize=20)
axes.set_xlabel(x_axis,fontsize=16)
axes.set_ylabel(y_axis,fontsize=16)
if x_log:
axes.set_xscale('log')
axes.grid()
# axes.fill_between(
# val_values,
# train_scores_mean - train_scores_std,
# train_scores_mean + train_scores_std,
# alpha=0.1,
# color="r",
# )
# axes.fill_between(
# val_values,
# val_scores_mean - val_scores_std,
# val_scores_mean + val_scores_std,
# alpha=0.1,
# color="b",
# )
axes.plot(
val_values, train_scores_mean, "o-", color="r", label="Training"
)
axes.plot(
val_values, val_scores_mean, "o-", color="b", label="Cross-validation"
)
axes.legend(loc="best")
def plot_loss_curve(
loss_curve,
title,
y_axis,
x_axis,
axes=None,
x_log=None
):
axes.set_title(title,fontsize=20)
axes.set_xlabel(x_axis,fontsize=16)
axes.set_ylabel(y_axis,fontsize=16)
if x_log:
axes.set_xscale('log')
axes.grid()
axes.plot(
loss_curve, color="r", label="Training Loss"
)
axes.legend(loc="best")
def plot_learning_curve(
train_scores,
val_scores,
train_sizes,
title,
y_axis,
axes=None,
):
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
val_scores_mean = np.mean(val_scores, axis=1)
val_scores_std = np.std(val_scores, axis=1)
axes.set_title(title,fontsize=20)
axes.set_xlabel("Training examples",fontsize=16)
axes.set_ylabel(y_axis,fontsize=16)
axes.grid()
# axes.fill_between(
# train_sizes,
# train_scores_mean - train_scores_std,
# train_scores_mean + train_scores_std,
# alpha=0.1,
# color="r",
# )
# axes.fill_between(
# train_sizes,
# val_scores_mean - val_scores_std,
# val_scores_mean + val_scores_std,
# alpha=0.1,
# color="b",
# )
axes.plot(
train_sizes, train_scores_mean, "o-", color="r", label="Training"
)
axes.plot(
train_sizes, val_scores_mean, "o-", color="b", label="Cross-validation"
)
axes.legend(loc="best")
def plot_fit_time(
fit_times,
train_sizes,
title,
y_axis,
axes=None,
):
fit_times = fit_times*1000
fit_times_mean = np.mean(fit_times, axis=1)
fit_times_std = np.std(fit_times, axis=1)
axes.set_title(title,fontsize=20)
axes.set_xlabel("Training examples",fontsize=16)
axes.set_ylabel("Fit Time (ms)",fontsize=16)
axes.grid()
# axes.fill_between(
# train_sizes,
# fit_times_mean - fit_times_std,
# fit_times_mean + fit_times_std,
# alpha=0.1,
# color="r",
# )
axes.plot(
train_sizes, fit_times_mean, "o-", color="r"
)
def plot_predict_time(
predict_times,
train_sizes,
title,
y_axis,
axes=None,
):
predict_times = predict_times * 1000
predict_times_mean = np.mean(predict_times, axis=1)
predict_times_std = np.std(predict_times, axis=1)
axes.set_title(title,fontsize=20)
axes.set_xlabel("Training examples",fontsize=16)
axes.set_ylabel("Predict Time (ms)",fontsize=16)
axes.grid()
# axes.fill_between(
# train_sizes,
# predict_times_mean - predict_times_std,
# predict_times_mean + predict_times_std,
# alpha=0.1,
# color="r",
# )
axes.plot(
train_sizes, predict_times_mean, "o-", color="r"
)