[QA] Remove utils.py
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Jensun Ravichandran
parent
70cf17607e
commit
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"""Utilities that provide various small functionalities."""
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import os
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import pickle
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import sys
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from time import time
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import matplotlib.pyplot as plt
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import numpy as np
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def progressbar(title, value, end, bar_width=20):
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percent = float(value) / end
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arrow = "=" * int(round(percent * bar_width) - 1) + ">"
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spaces = "." * (bar_width - len(arrow))
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sys.stdout.write("\r{}: [{}] {}%".format(title, arrow + spaces,
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int(round(percent * 100))))
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sys.stdout.flush()
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if percent == 1.0:
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print()
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def prettify_string(inputs, start="", sep=" ", end="\n"):
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outputs = start + " ".join(inputs.split()) + end
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return outputs
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def pretty_print(inputs):
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print(prettify_string(inputs))
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def writelog(self, *logs, logdir="./logs", logfile="run.txt"):
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f = os.path.join(logdir, logfile)
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with open(f, "a+") as fh:
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for log in logs:
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fh.write(log)
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fh.write("\n")
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def start_tensorboard(self, logdir="./logs"):
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cmd = f"tensorboard --logdir={logdir} --port=6006"
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os.system(cmd)
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def make_directory(save_dir):
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if not os.path.exists(save_dir):
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print(f"Making directory {save_dir}.")
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os.mkdir(save_dir)
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def make_gif(filenames, duration, output_file=None):
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try:
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import imageio
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except ModuleNotFoundError as e:
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print("Please install Protoflow with [other] extra requirements.")
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raise (e)
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images = list()
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for filename in filenames:
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images.append(imageio.imread(filename))
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if not output_file:
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output_file = f"makegif.gif"
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if images:
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imageio.mimwrite(output_file, images, duration=duration)
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def gif_from_dir(directory,
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duration,
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prefix="",
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output_file=None,
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verbose=True):
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images = os.listdir(directory)
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if verbose:
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print(f"Making gif from {len(images)} images under {directory}.")
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filenames = list()
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# Sort images
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images = sorted(
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images,
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key=lambda img: int(os.path.splitext(img)[0].replace(prefix, "")))
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for image in images:
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fname = os.path.join(directory, image)
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filenames.append(fname)
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if not output_file:
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output_file = os.path.join(directory, "makegif.gif")
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make_gif(filenames=filenames, duration=duration, output_file=output_file)
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def accuracy_score(y_true, y_pred):
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accuracy = np.sum(y_true == y_pred)
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normalized_acc = accuracy / float(len(y_true))
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return normalized_acc
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def predict_and_score(clf,
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x_test,
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y_test,
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verbose=False,
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title="Test accuracy"):
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y_pred = clf.predict(x_test)
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accuracy = np.sum(y_test == y_pred)
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normalized_acc = accuracy / float(len(y_test))
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if verbose:
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print(f"{title}: {normalized_acc * 100:06.04f}%")
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return normalized_acc
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def remove_nan_rows(arr):
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"""Remove all rows with `nan` values in `arr`."""
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mask = np.isnan(arr).any(axis=1)
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return arr[~mask]
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def remove_nan_cols(arr):
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"""Remove all columns with `nan` values in `arr`."""
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mask = np.isnan(arr).any(axis=0)
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return arr[~mask]
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def replace_in(arr, replacement_dict, inplace=False):
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"""Replace the keys found in `arr` with the values from
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the `replacement_dict`.
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"""
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if inplace:
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new_arr = arr
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else:
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import copy
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new_arr = copy.deepcopy(arr)
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for k, v in replacement_dict.items():
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new_arr[arr == k] = v
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return new_arr
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def train_test_split(data, train=0.7, val=0.15, shuffle=None, return_xy=False):
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"""Split a classification dataset in such a way so as to
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preserve the class distribution in subsamples of the dataset.
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"""
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if train + val > 1.0:
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raise ValueError("Invalid split values for train and val.")
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Y = data[:, -1]
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labels = set(Y)
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hist = dict()
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for l in labels:
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data_l = data[Y == l]
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nl = len(data_l)
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nl_train = int(nl * train)
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nl_val = int(nl * val)
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nl_test = nl - (nl_train + nl_val)
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hist[l] = (nl_train, nl_val, nl_test)
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train_data = list()
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val_data = list()
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test_data = list()
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for l, (nl_train, nl_val, nl_test) in hist.items():
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data_l = data[Y == l]
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if shuffle:
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np.random.shuffle(data_l)
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train_l = data_l[:nl_train]
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val_l = data_l[nl_train:nl_train + nl_val]
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test_l = data_l[nl_train + nl_val:nl_train + nl_val + nl_test]
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train_data.append(train_l)
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val_data.append(val_l)
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test_data.append(test_l)
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def _squash(data_list):
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data = np.array(data_list[0])
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for item in data_list[1:]:
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data = np.vstack((data, np.array(item)))
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return data
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train_data = _squash(train_data)
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if val_data:
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val_data = _squash(val_data)
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if test_data:
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test_data = _squash(test_data)
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if return_xy:
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x_train = train_data[:, :-1]
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y_train = train_data[:, -1]
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x_val = val_data[:, :-1]
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y_val = val_data[:, -1]
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x_test = test_data[:, :-1]
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y_test = test_data[:, -1]
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return (x_train, y_train), (x_val, y_val), (x_test, y_test)
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return train_data, val_data, test_data
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def class_histogram(data, title="Untitled"):
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plt.figure(title)
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plt.clf()
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plt.title(title)
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dist, counts = np.unique(data[:, -1], return_counts=True)
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plt.bar(dist, counts)
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plt.xticks(dist)
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print("Call matplotlib.pyplot.show() to see the plot.")
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def ntimer(n=10):
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"""Wraps a function which wraps another function to time it."""
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if n < 1:
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raise (Exception(f"Invalid n = {n} given."))
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def timer(func):
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"""Wraps `func` with a timer and returns the wrapped `func`."""
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def wrapper(*args, **kwargs):
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rv = None
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before = time()
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for _ in range(n):
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rv = func(*args, **kwargs)
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after = time()
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elapsed = after - before
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print(f"Elapsed: {elapsed*1e3:02.02f} ms")
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return rv
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return wrapper
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return timer
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def memoize(verbose=True):
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"""Wraps a function which wraps another function that memoizes."""
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def memoizer(func):
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"""Memoize (cache) return values of `func`.
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Wraps `func` and returns the wrapped `func` so that `func`
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is executed when the results are not available in the cache.
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"""
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cache = {}
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def wrapper(*args, **kwargs):
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t = (pickle.dumps(args), pickle.dumps(kwargs))
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if t not in cache:
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if verbose:
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print(f"Adding NEW rv {func.__name__}{args}{kwargs} "
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"to cache.")
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cache[t] = func(*args, **kwargs)
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else:
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if verbose:
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print(f"Using OLD rv {func.__name__}{args}{kwargs} "
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"from cache.")
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return cache[t]
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return wrapper
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return memoizer
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