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public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/pyplots/tracker_binary.py | import matplotlib.pyplot as plt
import torch
import torchmetrics
N = 10
num_updates = 10
num_steps = 5
w = torch.tensor([0.2, 0.8])
target = lambda it: torch.multinomial((it * w).softmax(dim=-1), 100, replacement=True)
preds = lambda it: (it * torch.randn(100)).sigmoid()
confmat = torchmetrics.ConfusionMatrix(task="binary")
roc = torchmetrics.ROC(task="binary")
tracker = torchmetrics.wrappers.MetricTracker(
torchmetrics.MetricCollection(
torchmetrics.Accuracy(task="binary"),
torchmetrics.Recall(task="binary"),
torchmetrics.Precision(task="binary"),
confmat,
roc,
)
)
fig = plt.figure(layout="constrained", figsize=(6.8, 4.8), dpi=500)
ax1 = plt.subplot(2, 2, 1)
ax2 = plt.subplot(2, 2, 2)
ax3 = plt.subplot(2, 2, (3, 4))
for step in range(num_steps):
tracker.increment()
for _ in range(N):
tracker.update(preds(step), target(step))
# get the results from all steps and extract for confusion matrix and roc
all_results = tracker.compute_all()
confmat.plot(val=all_results[-1]["BinaryConfusionMatrix"], ax=ax1)
roc.plot(all_results[-1]["BinaryROC"], ax=ax2)
scalar_results = [{k: v for k, v in ar.items() if isinstance(v, torch.Tensor) and v.numel() == 1} for ar in all_results]
tracker.plot(val=scalar_results, ax=ax3)
fig.show()
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/pyplots/collection_binary_together.py | import matplotlib.pyplot as plt
import torch
import torchmetrics
N = 10
num_updates = 10
num_steps = 5
w = torch.tensor([0.2, 0.8])
target = lambda it: torch.multinomial((it * w).softmax(dim=-1), 100, replacement=True)
preds = lambda it: torch.multinomial((it * w).softmax(dim=-1), 100, replacement=True)
collection = torchmetrics.MetricCollection(
torchmetrics.Accuracy(task="binary"),
torchmetrics.Recall(task="binary"),
torchmetrics.Precision(task="binary"),
)
values = []
fig, ax = plt.subplots(1, 1, figsize=(6.8, 4.8), dpi=500)
for step in range(num_steps):
for _ in range(N):
collection.update(preds(step), target(step))
values.append(collection.compute())
collection.reset()
collection.plot(val=values, ax=ax, together=True)
fig.tight_layout()
fig.show()
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/pyplots/binary_accuracy_multistep.py | import matplotlib.pyplot as plt
import torch
import torchmetrics
N = 10
num_updates = 10
num_steps = 5
w = torch.tensor([0.2, 0.8])
target = lambda it: torch.multinomial((it * w).softmax(dim=-1), 100, replacement=True)
preds = lambda it: torch.multinomial((it * w).softmax(dim=-1), 100, replacement=True)
fig, ax = plt.subplots(1, 1, figsize=(6.8, 4.8), dpi=500)
metric = torchmetrics.Accuracy(task="binary")
values = []
for step in range(num_steps):
for _ in range(N):
metric.update(preds(step), target(step))
values.append(metric.compute()) # save value
metric.reset()
metric.plot(values, ax=ax)
fig.show()
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/_templates/theme_variables.jinja | {%- set external_urls = {
'github': 'https://github.com/Lightning-AI/torchmetrics',
'github_issues': 'https://github.com/Lightning-AI/torchmetrics/issues',
'contributing': 'https://github.com/Lightning-AI/torchmetrics/blob/master/.github/CONTRIBUTING.md',
'docs': 'https://lightning.ai/docs/torchmetrics/latest',
'twitter': 'https://twitter.com/PyTorchLightnin',
'discuss': 'https://pytorch-lightning.slack.com',
'previous_pytorch_versions': 'https://torchmetrics.rtfd.io/en/latest/',
'home': 'https://torchmetrics.rtfd.io/en/latest/',
'get_started': 'https://lightning.ai/docs/torchmetrics/latest/pages/quickstart.html',
'blog': 'https://www.pytorchlightning.ai/blog',
'support': 'https://github.com/Lightning-AI/torchmetrics/issues',
'community': 'https://pytorch-lightning.slack.com',
'forums': 'https://pytorch-lightning.slack.com',
}
-%}
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/_templates/classtemplate.rst | .. role:: hidden
:class: hidden-section
.. currentmodule:: {{ module }}
{{ name | underline}}
.. autoclass:: {{ name }}
:members:
..
autogenerated from source/_templates/classtemplate.rst
note it does not have :inherited-members:
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/_templates/layout.html | {% extends "!layout.html" %}
<link rel="canonical" href="{{ theme_canonical_url }}{{ pagename }}.html" />
{% block footer %} {{ super() }}
<script script type="text/javascript">
var collapsedSections = [
"Audio",
"Classification",
"Image",
"Detection",
"Pairwise",
"Regression",
"Retrieval",
"Text",
"Aggregation",
"Wrappers",
"API Reference",
"Community",
];
</script>
{% endblock %}
| 0 |
public_repos/torchmetrics/docs/source/_templates | public_repos/torchmetrics/docs/source/_templates/autosummary/module.rst | {{ name | escape | underline }}
.. currentmodule:: {{ fullname }}
{% block functions %}
{% if functions %}
.. rubric:: Functions
.. autosummary::
:nosignatures:
{% for item in functions %}
{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
{% block classes %}
{% if classes %}
.. rubric:: Classes
.. autosummary::
:nosignatures:
{% for item in classes %}
{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
{% block exceptions %}
{% if exceptions %}
.. rubric:: Exceptions
.. autosummary::
:nosignatures:
{% for item in exceptions %}
{{ item }}
{%- endfor %}
{% endif %}
{% endblock %}
.. automodule:: {{ fullname }}
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/_static/copybutton.js | /* Copied from the official Python docs: https://docs.python.org/3/_static/copybutton.js */
$(document).ready(function () {
/* Add a [>>>] button on the top-right corner of code samples to hide
* the >>> and ... prompts and the output and thus make the code
* copyable. */
var div = $(
".highlight-python .highlight," +
".highlight-python3 .highlight," +
".highlight-pycon .highlight," +
".highlight-default .highlight",
);
var pre = div.find("pre");
// get the styles from the current theme
pre.parent().parent().css("position", "relative");
var hide_text = "Hide the prompts and output";
var show_text = "Show the prompts and output";
var border_width = pre.css("border-top-width");
var border_style = pre.css("border-top-style");
var border_color = pre.css("border-top-color");
var button_styles = {
cursor: "pointer",
position: "absolute",
top: "0",
right: "0",
"border-color": border_color,
"border-style": border_style,
"border-width": border_width,
color: border_color,
"text-size": "75%",
"font-family": "monospace",
"padding-left": "0.2em",
"padding-right": "0.2em",
"border-radius": "0 3px 0 0",
};
// create and add the button to all the code blocks that contain >>>
div.each(function (index) {
var jthis = $(this);
if (jthis.find(".gp").length > 0) {
var button = $('<span class="copybutton">>>></span>');
button.css(button_styles);
button.attr("title", hide_text);
button.data("hidden", "false");
jthis.prepend(button);
}
// tracebacks (.gt) contain bare text elements that need to be
// wrapped in a span to work with .nextUntil() (see later)
jthis
.find("pre:has(.gt)")
.contents()
.filter(function () {
return this.nodeType == 3 && this.data.trim().length > 0;
})
.wrap("<span>");
});
// define the behavior of the button when it's clicked
$(".copybutton").click(function (e) {
e.preventDefault();
var button = $(this);
if (button.data("hidden") === "false") {
// hide the code output
button.parent().find(".go, .gp, .gt").hide();
button.next("pre").find(".gt").nextUntil(".gp, .go").css("visibility", "hidden");
button.css("text-decoration", "line-through");
button.attr("title", show_text);
button.data("hidden", "true");
} else {
// show the code output
button.parent().find(".go, .gp, .gt").show();
button.next("pre").find(".gt").nextUntil(".gp, .go").css("visibility", "visible");
button.css("text-decoration", "none");
button.attr("title", hide_text);
button.data("hidden", "false");
}
});
});
| 0 |
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public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/word_info_preserved.rst | .. customcarditem::
:header: Word Information Preserved
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
####################
Word Info. Preserved
####################
Module Interface
________________
.. autoclass:: torchmetrics.text.WordInfoPreserved
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.word_information_preserved
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/edit.rst | .. customcarditem::
:header: Edit Distance
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
#############
Edit Distance
#############
Module Interface
________________
.. autoclass:: torchmetrics.text.EditDistance
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.edit_distance
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/char_error_rate.rst | .. customcarditem::
:header: Character Error Rate
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
###############
Char Error Rate
###############
Module Interface
________________
.. autoclass:: torchmetrics.text.CharErrorRate
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.char_error_rate
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/word_info_lost.rst | .. customcarditem::
:header: Word Information Lost
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
###############
Word Info. Lost
###############
Module Interface
________________
.. autoclass:: torchmetrics.text.WordInfoLost
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.word_information_lost
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/squad.rst | .. customcarditem::
:header: SQuAD
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
#####
SQuAD
#####
Module Interface
________________
.. autoclass:: torchmetrics.text.SQuAD
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.squad
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/rouge_score.rst | .. customcarditem::
:header: ROUGE Score
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
###########
ROUGE Score
###########
Module Interface
________________
.. autoclass:: torchmetrics.text.rouge.ROUGEScore
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.rouge.rouge_score
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/translation_edit_rate.rst | .. customcarditem::
:header: Translation Edit Rate (TER)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
###########################
Translation Edit Rate (TER)
###########################
Module Interface
________________
.. autoclass:: torchmetrics.text.TranslationEditRate
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.translation_edit_rate
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/bert_score.rst | .. customcarditem::
:header: BERT Score
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
##########
BERT Score
##########
Module Interface
________________
.. autoclass:: torchmetrics.text.bert.BERTScore
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.bert.bert_score
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/perplexity.rst | .. customcarditem::
:header: Perplexity
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
##########
Perplexity
##########
Module Interface
________________
.. autoclass:: torchmetrics.text.perplexity.Perplexity
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.perplexity.perplexity
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/extended_edit_distance.rst | .. customcarditem::
:header: Extended Edit Distance
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
######################
Extended Edit Distance
######################
Module Interface
________________
.. autoclass:: torchmetrics.text.ExtendedEditDistance
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.extended_edit_distance
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/bleu_score.rst | .. customcarditem::
:header: BLEU Score
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
##########
BLEU Score
##########
Module Interface
________________
.. autoclass:: torchmetrics.text.BLEUScore
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.bleu_score
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/sacre_bleu_score.rst | .. customcarditem::
:header: Sacre BLEU Score
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
################
Sacre BLEU Score
################
Module Interface
________________
.. autoclass:: torchmetrics.text.SacreBLEUScore
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.sacre_bleu_score
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/infolm.rst | .. customcarditem::
:header: InfoLM
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
######
InfoLM
######
Module Interface
________________
.. autoclass:: torchmetrics.text.infolm.InfoLM
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.infolm.infolm
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/match_error_rate.rst | .. customcarditem::
:header: Match Error Rate
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
################
Match Error Rate
################
Module Interface
________________
.. autoclass:: torchmetrics.text.MatchErrorRate
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.match_error_rate
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/chrf_score.rst | .. customcarditem::
:header: ChrF Score
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
##########
ChrF Score
##########
Module Interface
________________
.. autoclass:: torchmetrics.text.CHRFScore
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.chrf_score
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/text/word_error_rate.rst | .. customcarditem::
:header: Word Error Rate
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/summarization.svg
:tags: Text
.. include:: ../links.rst
###############
Word Error Rate
###############
Module Interface
________________
.. autoclass:: torchmetrics.text.WordErrorRate
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.text.word_error_rate
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/wrappers/running.rst | .. customcarditem::
:header: Running
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/graph_classification.svg
:tags: Wrappers
.. include:: ../links.rst
#######
Running
#######
Module Interface
________________
.. autoclass:: torchmetrics.wrappers.Running
:exclude-members: update, compute
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/wrappers/classwise_wrapper.rst | .. customcarditem::
:header: Classwise Wrapper
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/graph_classification.svg
:tags: Wrappers
.. include:: ../links.rst
#################
Classwise Wrapper
#################
Module Interface
________________
.. autoclass:: torchmetrics.wrappers.ClasswiseWrapper
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/wrappers/min_max.rst | .. customcarditem::
:header: Min / Max
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/graph_classification.svg
:tags: Wrappers
.. include:: ../links.rst
#########
Min / Max
#########
Module Interface
________________
.. autoclass:: torchmetrics.wrappers.MinMaxMetric
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/wrappers/bootstrapper.rst | .. customcarditem::
:header: Bootstrapper
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/graph_classification.svg
:tags: Wrappers
.. include:: ../links.rst
############
Bootstrapper
############
Module Interface
________________
.. autoclass:: torchmetrics.wrappers.BootStrapper
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/wrappers/metric_tracker.rst | .. customcarditem::
:header: Metric Tracker
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/graph_classification.svg
:tags: Wrappers
.. include:: ../links.rst
##############
Metric Tracker
##############
Module Interface
________________
.. autoclass:: torchmetrics.wrappers.MetricTracker
:exclude-members: update, compute
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/wrappers/multi_task_wrapper.rst | .. customcarditem::
:header: Multi-task Wrapper
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/graph_classification.svg
:tags: Wrappers
.. include:: ../links.rst
####################
Multi-task Wrapper
####################
Module Interface
________________
.. autoclass:: torchmetrics.wrappers.MultitaskWrapper
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/wrappers/multi_output_wrapper.rst | .. customcarditem::
:header: Multi-output Wrapper
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/graph_classification.svg
:tags: Wrappers
.. include:: ../links.rst
####################
Multi-output Wrapper
####################
Module Interface
________________
.. autoclass:: torchmetrics.wrappers.MultioutputWrapper
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/complex_scale_invariant_signal_noise_ratio.rst | .. customcarditem::
:header: Complex Scale-Invariant Signal-to-Noise Ratio (C-SI-SNR)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
########################################################
Complex Scale-Invariant Signal-to-Noise Ratio (C-SI-SNR)
########################################################
Module Interface
________________
.. autoclass:: torchmetrics.audio.ComplexScaleInvariantSignalNoiseRatio
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.complex_scale_invariant_signal_noise_ratio
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/signal_noise_ratio.rst | .. customcarditem::
:header: Signal-to-Noise Ratio (SNR)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
###########################
Signal-to-Noise Ratio (SNR)
###########################
Module Interface
________________
.. autoclass:: torchmetrics.audio.SignalNoiseRatio
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.signal_noise_ratio
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/scale_invariant_signal_noise_ratio.rst | .. customcarditem::
:header: Scale-Invariant Signal-to-Noise Ratio (SI-SNR)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
##############################################
Scale-Invariant Signal-to-Noise Ratio (SI-SNR)
##############################################
Module Interface
________________
.. autoclass:: torchmetrics.audio.ScaleInvariantSignalNoiseRatio
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.scale_invariant_signal_noise_ratio
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/speech_reverberation_modulation_energy_ratio.rst | .. customcarditem::
:header: Speech-to-Reverberation Modulation Energy Ratio (SRMR)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
######################################################
Speech-to-Reverberation Modulation Energy Ratio (SRMR)
######################################################
Module Interface
________________
.. autoclass:: torchmetrics.audio.srmr.SpeechReverberationModulationEnergyRatio
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.srmr.speech_reverberation_modulation_energy_ratio
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/scale_invariant_signal_distortion_ratio.rst | .. customcarditem::
:header: Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
###################################################
Scale-Invariant Signal-to-Distortion Ratio (SI-SDR)
###################################################
Module Interface
________________
.. autoclass:: torchmetrics.audio.ScaleInvariantSignalDistortionRatio
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.scale_invariant_signal_distortion_ratio
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/permutation_invariant_training.rst | .. customcarditem::
:header: Permutation Invariant Training (PIT)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
####################################
Permutation Invariant Training (PIT)
####################################
Module Interface
________________
.. autoclass:: torchmetrics.audio.PermutationInvariantTraining
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.permutation_invariant_training
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/signal_distortion_ratio.rst | .. customcarditem::
:header: Signal to Distortion Ratio (SDR)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
################################
Signal to Distortion Ratio (SDR)
################################
Module Interface
________________
.. autoclass:: torchmetrics.audio.SignalDistortionRatio
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.signal_distortion_ratio
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/short_time_objective_intelligibility.rst | .. customcarditem::
:header: Short-Time Objective Intelligibility (STOI)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
###########################################
Short-Time Objective Intelligibility (STOI)
###########################################
Module Interface
________________
.. autoclass:: torchmetrics.audio.stoi.ShortTimeObjectiveIntelligibility
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.stoi.short_time_objective_intelligibility
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/source_aggregated_signal_distortion_ratio.rst | .. customcarditem::
:header: Source Aggregated Signal-to-Distortion Ratio (SA-SDR)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
#####################################################
Source Aggregated Signal-to-Distortion Ratio (SA-SDR)
#####################################################
Module Interface
________________
.. autoclass:: torchmetrics.audio.sdr.SourceAggregatedSignalDistortionRatio
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.sdr.source_aggregated_signal_distortion_ratio
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/audio/perceptual_evaluation_speech_quality.rst | .. customcarditem::
:header: Perceptual Evaluation of Speech Quality (PESQ)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/audio_classification.svg
:tags: Audio
.. include:: ../links.rst
##############################################
Perceptual Evaluation of Speech Quality (PESQ)
##############################################
Module Interface
________________
.. autoclass:: torchmetrics.audio.pesq.PerceptualEvaluationSpeechQuality
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.audio.pesq.perceptual_evaluation_speech_quality
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/references/utilities.rst | .. role:: hidden
:class: hidden-section
######################
torchmetrics.utilities
######################
In the following is listed public utility functions that may be beneficial to use in your own code. These functions are
not part of the public API and may change at any time.
***************************
torchmetrics.utilities.data
***************************
The `data` utilities are used to help with data manipulation, such as converting labels in classification from one
format to another.
select_topk
~~~~~~~~~~~
.. autofunction:: torchmetrics.utilities.data.select_topk
to_categorical
~~~~~~~~~~~~~~
.. autofunction:: torchmetrics.utilities.data.to_categorical
to_onehot
~~~~~~~~~
.. autofunction:: torchmetrics.utilities.data.to_onehot
dim_zero_cat
~~~~~~~~~~~~
.. autofunction:: torchmetrics.utilities.data.dim_zero_cat
dim_zero_max
~~~~~~~~~~~~
.. autofunction:: torchmetrics.utilities.data.dim_zero_max
dim_zero_mean
~~~~~~~~~~~~~
.. autofunction:: torchmetrics.utilities.data.dim_zero_mean
dim_zero_min
~~~~~~~~~~~~
.. autofunction:: torchmetrics.utilities.data.dim_zero_min
dim_zero_sum
~~~~~~~~~~~~
.. autofunction:: torchmetrics.utilities.data.dim_zero_sum
**********************************
torchmetrics.utilities.distributed
**********************************
The `distributed` utilities are used to help with synchronization of metrics across multiple processes.
gather_all_tensors
~~~~~~~~~~~~~~~~~~
.. autofunction:: torchmetrics.utilities.distributed.gather_all_tensors
:noindex:
*********************************
torchmetrics.utilities.exceptions
*********************************
TorchMetricsUserError
~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: torchmetrics.utilities.exceptions.TorchMetricsUserError
TorchMetricsUserWarning
~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: torchmetrics.utilities.exceptions.TorchMetricsUserWarning
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/references/metric.rst | .. role:: hidden
:class: hidden-section
###################
torchmetrics.Metric
###################
The base ``Metric`` class is an abstract base class that are used as the building block for all other Module
metrics.
.. autoclass:: torchmetrics.Metric
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/retrieval/r_precision.rst | .. customcarditem::
:header: Retrieval R-Precision
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/text_classification.svg
:tags: Retrieval
.. include:: ../links.rst
#####################
Retrieval R-Precision
#####################
Module Interface
________________
.. autoclass:: torchmetrics.retrieval.RetrievalRPrecision
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.retrieval.retrieval_r_precision
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/retrieval/mrr.rst | .. customcarditem::
:header: Retrieval Mean Reciprocal Rank (MRR)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/text_classification.svg
:tags: Retrieval
.. include:: ../links.rst
####################################
Retrieval Mean Reciprocal Rank (MRR)
####################################
Module Interface
________________
.. autoclass:: torchmetrics.retrieval.RetrievalMRR
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.retrieval.retrieval_reciprocal_rank
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/retrieval/map.rst | .. customcarditem::
:header: Retrieval Mean Average Precision (MAP)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/text_classification.svg
:tags: Retrieval
.. include:: ../links.rst
######################################
Retrieval Mean Average Precision (MAP)
######################################
Module Interface
________________
.. autoclass:: torchmetrics.retrieval.RetrievalMAP
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.retrieval.retrieval_average_precision
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/retrieval/fall_out.rst | .. customcarditem::
:header: Retrieval Fall-Out
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/text_classification.svg
:tags: Retrieval
.. include:: ../links.rst
##################
Retrieval Fall-Out
##################
Module Interface
________________
.. autoclass:: torchmetrics.retrieval.RetrievalFallOut
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.retrieval.retrieval_fall_out
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/retrieval/recall.rst | .. customcarditem::
:header: Retrieval Recall
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/text_classification.svg
:tags: Retrieval
.. include:: ../links.rst
################
Retrieval Recall
################
Module Interface
________________
.. autoclass:: torchmetrics.retrieval.RetrievalRecall
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.retrieval.retrieval_recall
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/retrieval/hit_rate.rst | .. customcarditem::
:header: Retrieval Hit Rate
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/text_classification.svg
:tags: Retrieval
.. include:: ../links.rst
##################
Retrieval Hit Rate
##################
Module Interface
________________
.. autoclass:: torchmetrics.retrieval.RetrievalHitRate
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.retrieval.retrieval_hit_rate
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/retrieval/precision.rst | .. customcarditem::
:header: Retrieval Precision
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/text_classification.svg
:tags: Retrieval
.. include:: ../links.rst
###################
Retrieval Precision
###################
Module Interface
________________
.. autoclass:: torchmetrics.retrieval.RetrievalPrecision
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.retrieval.retrieval_precision
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/retrieval/normalized_dcg.rst | .. customcarditem::
:header: Retrieval Normalized Discounted Cumulative Gain (DCG)
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/text_classification.svg
:tags: Retrieval
.. include:: ../links.rst
########################
Retrieval Normalized DCG
########################
Module Interface
________________
.. autoclass:: torchmetrics.retrieval.RetrievalNormalizedDCG
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.retrieval.retrieval_normalized_dcg
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/retrieval/precision_recall_curve.rst | .. customcarditem::
:header: Precision Recall Curve
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/text_classification.svg
:tags: Retrieval
.. include:: ../links.rst
######################
Precision Recall Curve
######################
Module Interface
________________
.. autoclass:: torchmetrics.retrieval.RetrievalPrecisionRecallCurve
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.retrieval.retrieval_precision_recall_curve
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/multimodal/clip_score.rst | .. customcarditem::
:header: CLIP Score
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/image_classification.svg
:tags: Image
.. include:: ../links.rst
##########
CLIP Score
##########
Module Interface
________________
.. autoclass:: torchmetrics.multimodal.clip_score.CLIPScore
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.multimodal.clip_score.clip_score
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/multimodal/clip_iqa.rst | .. customcarditem::
:header: CLIP IQA
:image: https://pl-flash-data.s3.amazonaws.com/assets/thumbnails/image_classification.svg
:tags: Image
.. include:: ../links.rst
########################################
CLIP Image Quality Assessment (CLIP-IQA)
########################################
Module Interface
________________
.. autoclass:: torchmetrics.multimodal.CLIPImageQualityAssessment
:noindex:
:exclude-members: update, compute
Functional Interface
____________________
.. autofunction:: torchmetrics.functional.multimodal.clip_image_quality_assessment
:noindex:
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/pages/lightning.rst | .. testsetup:: *
import torch
from torch.nn import Module
from lightning.pytorch import LightningModule
from torchmetrics import Metric
#################################
TorchMetrics in PyTorch Lightning
#################################
TorchMetrics was originally created as part of `PyTorch Lightning <https://github.com/Lightning-AI/pytorch-lightning>`_, a powerful deep learning research
framework designed for scaling models without boilerplate.
.. note::
TorchMetrics always offers compatibility with the last 2 major PyTorch Lightning versions, but we recommend to always keep both frameworks
up-to-date for the best experience.
While TorchMetrics was built to be used with native PyTorch, using TorchMetrics with Lightning offers additional benefits:
* Modular metrics are automatically placed on the correct device when properly defined inside a LightningModule.
This means that your data will always be placed on the same device as your metrics. No need to call ``.to(device)`` anymore!
* Native support for logging metrics in Lightning using
`self.log <https://pytorch-lightning.readthedocs.io/en/stable/extensions/logging.html#logging-from-a-lightningmodule>`_ inside
your LightningModule.
* The ``.reset()`` method of the metric will automatically be called at the end of an epoch.
The example below shows how to use a metric in your `LightningModule <https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html>`_:
.. testcode:: python
class MyModel(LightningModule):
def __init__(self, num_classes):
...
self.accuracy = torchmetrics.classification.Accuracy(task="multiclass", num_classes=num_classes)
def training_step(self, batch, batch_idx):
x, y = batch
preds = self(x)
...
# log step metric
self.accuracy(preds, y)
self.log('train_acc_step', self.accuracy)
...
def on_train_epoch_end(self):
# log epoch metric
self.log('train_acc_epoch', self.accuracy)
Metric logging in Lightning happens through the ``self.log`` or ``self.log_dict`` method. Both methods only support the
logging of *scalar-tensors*. While the vast majority of metrics in torchmetrics returns a scalar tensor, some metrics
such as :class:`~torchmetrics.classification.confusion_matrix.ConfusionMatrix`,
:class:`~torchmetrics.classification.roc.ROC`,
:class:`~torchmetrics.detection.mean_ap.MeanAveragePrecision`, :class:`~torchmetrics.text.rouge.ROUGEScore` return
outputs that are non-scalar tensors (often dicts or list of tensors) and should therefore be dealt with separately.
For info about the return type and shape please look at the documentation for the ``compute`` method for each metric
you want to log.
********************
Logging TorchMetrics
********************
Logging metrics can be done in two ways: either logging the metric object directly or the computed metric values.
When :class:`~torchmetrics.Metric` objects, which return a scalar tensor are logged directly in Lightning using the
LightningModule `self.log <https://pytorch-lightning.readthedocs.io/en/stable/extensions/logging.html#logging-from-a-lightningmodule>`_
method, Lightning will log the metric based on ``on_step`` and ``on_epoch`` flags present in ``self.log(...)``. If
``on_epoch`` is True, the logger automatically logs the end of epoch metric value by calling ``.compute()``.
.. note::
``sync_dist``, ``sync_dist_group`` and ``reduce_fx`` flags from ``self.log(...)`` don't affect the metric logging
in any manner. The metric class contains its own distributed synchronization logic.
This however is only true for metrics that inherit the base class ``Metric``,
and thus the functional metric API provides no support for in-built distributed synchronization
or reduction functions.
.. testcode:: python
class MyModule(LightningModule):
def __init__(self, num_classes):
...
self.train_acc = torchmetrics.classification.Accuracy(task="multiclass", num_classes=num_classes)
self.valid_acc = torchmetrics.classification.Accuracy(task="multiclass", num_classes=num_classes)
def training_step(self, batch, batch_idx):
x, y = batch
preds = self(x)
...
self.train_acc(preds, y)
self.log('train_acc', self.train_acc, on_step=True, on_epoch=False)
def validation_step(self, batch, batch_idx):
logits = self(x)
...
self.valid_acc(logits, y)
self.log('valid_acc', self.valid_acc, on_step=True, on_epoch=True)
As an alternative to logging the metric object and letting Lightning take care of when to reset the metric etc. you can
also manually log the output of the metrics.
.. testcode:: python
class MyModule(LightningModule):
def __init__(self):
...
self.train_acc = torchmetrics.classification.Accuracy(task="multiclass", num_classes=num_classes)
self.valid_acc = torchmetrics.classification.Accuracy(task="multiclass", num_classes=num_classes)
def training_step(self, batch, batch_idx):
x, y = batch
preds = self(x)
...
batch_value = self.train_acc(preds, y)
self.log('train_acc_step', batch_value)
def on_train_epoch_end(self):
self.train_acc.reset()
def validation_step(self, batch, batch_idx):
logits = self(x)
...
self.valid_acc.update(logits, y)
def on_validation_epoch_end(self, outputs):
self.log('valid_acc_epoch', self.valid_acc.compute())
self.valid_acc.reset()
Note that logging metrics this way will require you to manually reset the metrics at the end of the epoch yourself.
In general, we recommend logging the metric object to make sure that metrics are correctly computed and reset.
Additionally, we highly recommend that the two ways of logging are not mixed as it can lead to wrong results.
.. note::
When using any Modular metric, calling ``self.metric(...)`` or ``self.metric.forward(...)`` serves the dual purpose
of calling ``self.metric.update()`` on its input and simultaneously returning the metric value over the provided
input. So if you are logging a metric *only* on epoch-level (as in the example above), it is recommended to call
``self.metric.update()`` directly to avoid the extra computation.
.. testcode:: python
class MyModule(LightningModule):
def __init__(self, num_classes):
...
self.valid_acc = torchmetrics.classification.Accuracy(task="multiclass", num_classes=num_classes)
def validation_step(self, batch, batch_idx):
logits = self(x)
...
self.valid_acc.update(logits, y)
self.log('valid_acc', self.valid_acc, on_step=True, on_epoch=True)
***************
Common Pitfalls
***************
The following contains a list of pitfalls to be aware of:
* Modular metrics contain internal states that should belong to only one DataLoader. In case you are using multiple DataLoaders,
it is recommended to initialize a separate modular metric instances for each DataLoader and use them separately. The same holds
for using separate metrics for training, validation and testing.
.. testcode:: python
class MyModule(LightningModule):
def __init__(self, num_classes):
...
self.val_acc = nn.ModuleList(
[torchmetrics.classification.Accuracy(task="multiclass", num_classes=num_classes) for _ in range(2)]
)
def val_dataloader(self):
return [DataLoader(...), DataLoader(...)]
def validation_step(self, batch, batch_idx, dataloader_idx):
x, y = batch
preds = self(x)
...
self.val_acc[dataloader_idx](preds, y)
self.log('val_acc', self.val_acc[dataloader_idx])
* Mixing the two logging methods by calling ``self.log("val", self.metric)`` in ``{training|validation|test}_step``
method and then calling ``self.log("val", self.metric.compute())`` in the corresponding
``on_{train|validation|test}_epoch_end`` method.
Because the object is logged in the first case, Lightning will reset the metric before calling the second line leading
to errors or nonsense results.
* Calling ``self.log("val", self.metric(preds, target))`` with the intention of logging the metric object. Because
``self.metric(preds, target)`` corresponds to calling the forward method, this will return a tensor and not the
metric object. Such logging will be wrong in this case. Instead, it is essential to separate into several lines:
.. testcode:: python
def training_step(self, batch, batch_idx):
x, y = batch
preds = self(x)
...
# log step metric
self.accuracy(preds, y) # compute metrics
self.log('train_acc_step', self.accuracy) # log metric object
* Using :class:`~torchmetrics.wrappers.MetricTracker` wrapper with Lightning is a special case, because the wrapper in itself is not a metric
i.e. it does not inherit from the base :class:`~torchmetrics.Metric` class but instead from :class:`~torch.nn.ModuleList`. Thus,
to log the output of this metric one needs to manually log the returned values (not the object) using ``self.log``
and for epoch level logging this should be done in the appropriate ``on_{train|validation|test}_epoch_end`` method.
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/pages/quickstart.rst | ###########
Quick Start
###########
TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. It offers:
* A standardized interface to increase reproducibility
* Reduces Boilerplate
* Distributed-training compatible
* Rigorously tested
* Automatic accumulation over batches
* Automatic synchronization between multiple devices
You can use TorchMetrics in any PyTorch model, or within `PyTorch Lightning <https://pytorch-lightning.readthedocs.io/en/stable/>`_ to enjoy additional features:
* This means that your data will always be placed on the same device as your metrics.
* Native support for logging metrics in Lightning to reduce even more boilerplate.
Install
*******
You can install TorchMetrics using pip or conda:
.. code-block:: bash
# Python Package Index (PyPI)
pip install torchmetrics
# Conda
conda install -c conda-forge torchmetrics
Eventually if there is a missing PyTorch wheel for your OS or Python version you can simply compile `PyTorch from source <https://github.com/pytorch/pytorch>`_:
.. code-block:: bash
# Optional if you do not need compile GPU support
export USE_CUDA=0 # just to keep it simple
# you can install the latest state from master
pip install git+https://github.com/pytorch/pytorch.git
# OR set a particular PyTorch release
pip install git+https://github.com/pytorch/pytorch.git@<release-tag>
# and finalize with installing TorchMetrics
pip install torchmetrics
Using TorchMetrics
******************
Functional metrics
~~~~~~~~~~~~~~~~~~
Similar to `torch.nn <https://pytorch.org/docs/stable/nn>`_, most metrics have both a class-based and a functional version.
The functional versions implement the basic operations required for computing each metric.
They are simple python functions that as input take `torch.tensors <https://pytorch.org/docs/stable/tensors.html>`_
and return the corresponding metric as a ``torch.tensor``.
The code-snippet below shows a simple example for calculating the accuracy using the functional interface:
.. testcode::
import torch
# import our library
import torchmetrics
# simulate a classification problem
preds = torch.randn(10, 5).softmax(dim=-1)
target = torch.randint(5, (10,))
acc = torchmetrics.functional.accuracy(preds, target, task="multiclass", num_classes=5)
Module metrics
~~~~~~~~~~~~~~
Nearly all functional metrics have a corresponding class-based metric that calls it a functional counterpart underneath.
The class-based metrics are characterized by having one or more internal metrics states (similar to the parameters of
the PyTorch module) that allow them to offer additional functionalities:
* Accumulation of multiple batches
* Automatic synchronization between multiple devices
* Metric arithmetic
The code below shows how to use the class-based interface:
.. testcode::
import torch
# import our library
import torchmetrics
# initialize metric
metric = torchmetrics.classification.Accuracy(task="multiclass", num_classes=5)
n_batches = 10
for i in range(n_batches):
# simulate a classification problem
preds = torch.randn(10, 5).softmax(dim=-1)
target = torch.randint(5, (10,))
# metric on current batch
acc = metric(preds, target)
print(f"Accuracy on batch {i}: {acc}")
# metric on all batches using custom accumulation
acc = metric.compute()
print(f"Accuracy on all data: {acc}")
# Resetting internal state such that metric ready for new data
metric.reset()
.. testoutput::
:hide:
:options: +ELLIPSIS, +NORMALIZE_WHITESPACE
Accuracy on batch ...
Implementing your own metric
****************************
Implementing your own metric is as easy as subclassing a :class:`torch.nn.Module`. Simply, subclass :class:`~torchmetrics.Metric` and do the following:
1. Implement ``__init__`` where you call ``self.add_state`` for every internal state that is needed for the metrics computations
2. Implement ``update`` method, where all logic that is necessary for updating metric states go
3. Implement ``compute`` method, where the final metric computations happens
For practical examples and more info about implementing a metric, please see this :ref:`page <implement>`.
Development Environment
~~~~~~~~~~~~~~~~~~~~~~~
TorchMetrics provides a `Devcontainer <https://code.visualstudio.com/docs/remote/containers>`_ configuration for `Visual Studio Code <https://code.visualstudio.com/>`_ to use a `Docker container <https://www.docker.com/>`_ as a pre-configured development environment.
This avoids struggles setting up a development environment and makes them reproducible and consistent.
Please follow the `installation instructions <https://code.visualstudio.com/docs/remote/containers#_installation>`_ and make yourself familiar with the `container tutorials <https://code.visualstudio.com/docs/remote/containers-tutorial>`_ if you want to use them.
In order to use GPUs, you can enable them within the ``.devcontainer/devcontainer.json`` file.
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/pages/overview.rst | .. testsetup:: *
import torch
from lightning.pytorch import LightningModule
##################
Structure Overview
##################
TorchMetrics is a Metrics API created for easy metric development and usage in
PyTorch and PyTorch Lightning. It is rigorously tested for all edge cases and includes a growing list of
common metric implementations.
The metrics API provides ``update()``, ``compute()``, ``reset()`` functions to the user. The metric :ref:`base class <references/metric:torchmetrics.Metric>` inherits
:class:`torch.nn.Module` which allows us to call ``metric(...)`` directly. The ``forward()`` method of the base ``Metric`` class
serves the dual purpose of calling ``update()`` on its input and simultaneously returning the value of the metric over the
provided input.
These metrics work with DDP in PyTorch and PyTorch Lightning by default. When ``.compute()`` is called in
distributed mode, the internal state of each metric is synced and reduced across each process, so that the
logic present in ``.compute()`` is applied to state information from all processes.
This metrics API is independent of PyTorch Lightning. Metrics can directly be used in PyTorch as shown in the example:
.. code-block:: python
from torchmetrics.classification import BinaryAccuracy
train_accuracy = BinaryAccuracy()
valid_accuracy = BinaryAccuracy()
for epoch in range(epochs):
for x, y in train_data:
y_hat = model(x)
# training step accuracy
batch_acc = train_accuracy(y_hat, y)
print(f"Accuracy of batch{i} is {batch_acc}")
for x, y in valid_data:
y_hat = model(x)
valid_accuracy.update(y_hat, y)
# total accuracy over all training batches
total_train_accuracy = train_accuracy.compute()
# total accuracy over all validation batches
total_valid_accuracy = valid_accuracy.compute()
print(f"Training acc for epoch {epoch}: {total_train_accuracy}")
print(f"Validation acc for epoch {epoch}: {total_valid_accuracy}")
# Reset metric states after each epoch
train_accuracy.reset()
valid_accuracy.reset()
.. note::
Metrics contain internal states that keep track of the data seen so far.
Do not mix metric states across training, validation and testing.
It is highly recommended to re-initialize the metric per mode as
shown in the examples above.
.. note::
Metric states are **not** added to the models ``state_dict`` by default.
To change this, after initializing the metric, the method ``.persistent(mode)`` can
be used to enable (``mode=True``) or disable (``mode=False``) this behaviour.
.. note::
Due to specialized logic around metric states, we in general do **not** recommend
that metrics are initialized inside other metrics (nested metrics), as this can lead
to weird behaviour. Instead consider subclassing a metric or use
``torchmetrics.MetricCollection``.
*******************
Metrics and devices
*******************
Metrics are simple subclasses of :class:`~torch.nn.Module` and their metric states behave
similar to buffers and parameters of modules. This means that metrics states should
be moved to the same device as the input of the metric:
.. code-block:: python
from torchmetrics.classification import BinaryAccuracy
target = torch.tensor([1, 1, 0, 0], device=torch.device("cuda", 0))
preds = torch.tensor([0, 1, 0, 0], device=torch.device("cuda", 0))
# Metric states are always initialized on cpu, and needs to be moved to
# the correct device
confmat = BinaryAccuracy().to(torch.device("cuda", 0))
out = confmat(preds, target)
print(out.device) # cuda:0
However, when **properly defined** inside a :class:`~torch.nn.Module` or
`LightningModule <https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html>`_ the metric will
be automatically moved to the same device as the module when using ``.to(device)``. Being
**properly defined** means that the metric is correctly identified as a child module of the
model (check ``.children()`` attribute of the model). Therefore, metrics cannot be placed
in native python ``list`` and ``dict``, as they will not be correctly identified
as child modules. Instead of ``list`` use :class:`~torch.nn.ModuleList` and instead of
``dict`` use :class:`~torch.nn.ModuleDict`. Furthermore, when working with multiple metrics
the native `MetricCollection`_ module can also be used to wrap multiple metrics.
.. testcode::
from torchmetrics import MetricCollection
from torchmetrics.classification import BinaryAccuracy
class MyModule(torch.nn.Module):
def __init__(self):
...
# valid ways metrics will be identified as child modules
self.metric1 = BinaryAccuracy()
self.metric2 = nn.ModuleList(BinaryAccuracy())
self.metric3 = nn.ModuleDict({'accuracy': BinaryAccuracy()})
self.metric4 = MetricCollection([BinaryAccuracy()]) # torchmetrics built-in collection class
def forward(self, batch):
data, target = batch
preds = self(data)
...
val1 = self.metric1(preds, target)
val2 = self.metric2[0](preds, target)
val3 = self.metric3['accuracy'](preds, target)
val4 = self.metric4(preds, target)
You can always check which device the metric is located on using the `.device` property.
*****************************
Metrics and memory management
*****************************
As stated before, metrics have states and those states take up a certain amount of memory depending on the metric.
In general metrics can be divided into two categories when we talk about memory management:
* Metrics with tensor states: These metrics only have states that are instances of :class:`~torch.Tensor`. When these
kind of metrics are updated the values of those tensors are updated. Importantly the size of the tensors is
**constant** meaning that regardless of how much data is passed to the metric, its memory footprint will not change.
* Metrics with list states: These metrics have at least one state that is a list, which gets tensors appended as the
metric is updated. Importantly the size of the list is therefore **not constant** and will grow. The growth depends
on the particular metric (some metrics only need to store a single value per sample, some much more).
You can always check the current metric state by accessing the `.metric_state` property, and checking if any of the
states are lists.
.. testcode::
import torch
from torchmetrics.regression import SpearmanCorrCoef
gen = torch.manual_seed(42)
metric = SpearmanCorrCoef()
metric(torch.rand(2,), torch.rand(2,))
print(metric.metric_state)
metric(torch.rand(2,), torch.rand(2,))
print(metric.metric_state)
.. testoutput::
:options: +NORMALIZE_WHITESPACE
{'preds': [tensor([0.8823, 0.9150])], 'target': [tensor([0.3829, 0.9593])]}
{'preds': [tensor([0.8823, 0.9150]), tensor([0.3904, 0.6009])], 'target': [tensor([0.3829, 0.9593]), tensor([0.2566, 0.7936])]}
In general we have a few recommendations for memory management:
* When done with a metric, we always recommend calling the `reset` method. The reason for this being that the python
garbage collector can struggle to totally clean the metric states if this is not done. In the worst case, this can
lead to a memory leak if multiple instances of the same metric for different purposes are created in the same script.
* Better to always try to reuse the same instance of a metric instead of initializing a new one. Calling the `reset` method
returns the metric to its initial state, and can therefore be used to reuse the same instance. However, we still
highly recommend to use **different** instances from training, validation and testing.
* If only the results on a batch level are needed e.g no aggregation or alternatively if you have a small dataset that
fits into iteration of evaluation, we can recommend using the functional API instead as it does not keep an internal
state and memory is therefore freed after each call.
See :ref:`Metric kwargs` for different advanced settings for controlling the memory footprint of metrics.
***********************************************
Metrics in Distributed Data Parallel (DDP) mode
***********************************************
When using metrics in `Distributed Data Parallel (DDP) <https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html>`_
mode, one should be aware that DDP will add additional samples to your dataset if the size of your dataset is
not equally divisible by ``batch_size * num_processors``. The added samples will always be replicates of datapoints
already in your dataset. This is done to secure an equal load for all processes. However, this has the consequence
that the calculated metric value will be slightly biased towards those replicated samples, leading to a wrong result.
During training and/or validation this may not be important, however it is highly recommended when evaluating
the test dataset to only run on a single gpu or use a `join <https://pytorch.org/docs/stable/_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join>`_
context in conjunction with DDP to prevent this behaviour.
****************************
Metrics and 16-bit precision
****************************
Most metrics in our collection can be used with 16-bit precision (``torch.half``) tensors. However, we have found
the following limitations:
* In general ``pytorch`` had better support for 16-bit precision much earlier on GPU than CPU. Therefore, we
recommend that anyone that want to use metrics with half precision on CPU, upgrade to at least pytorch v1.6
where support for operations such as addition, subtraction, multiplication etc. was added.
* Some metrics does not work at all in half precision on CPU. We have explicitly stated this in their docstring,
but they are also listed below:
- :ref:`image/peak_signal_noise_ratio:Peak Signal-to-Noise Ratio (PSNR)`
- :ref:`image/structural_similarity:Structural Similarity Index Measure (SSIM)`
- :ref:`regression/kl_divergence:KL Divergence`
You can always check the precision/dtype of the metric by checking the `.dtype` property.
*****************
Metric Arithmetic
*****************
Metrics support most of python built-in operators for arithmetic, logic and bitwise operations.
For example for a metric that should return the sum of two different metrics, implementing a new metric is an
overhead that is not necessary. It can now be done with:
.. code-block:: python
first_metric = MyFirstMetric()
second_metric = MySecondMetric()
new_metric = first_metric + second_metric
``new_metric.update(*args, **kwargs)`` now calls update of ``first_metric`` and ``second_metric``. It forwards
all positional arguments but forwards only the keyword arguments that are available in respective metric's update
declaration. Similarly ``new_metric.compute()`` now calls compute of ``first_metric`` and ``second_metric`` and
adds the results up. It is important to note that all implemented operations always return a new metric object. This means
that the line ``first_metric == second_metric`` will not return a bool indicating if ``first_metric`` and ``second_metric``
is the same metric, but will return a new metric that checks if the ``first_metric.compute() == second_metric.compute()``.
This pattern is implemented for the following operators (with ``a`` being metrics and ``b`` being metrics, tensors, integer or floats):
* Addition (``a + b``)
* Bitwise AND (``a & b``)
* Equality (``a == b``)
* Floordivision (``a // b``)
* Greater Equal (``a >= b``)
* Greater (``a > b``)
* Less Equal (``a <= b``)
* Less (``a < b``)
* Matrix Multiplication (``a @ b``)
* Modulo (``a % b``)
* Multiplication (``a * b``)
* Inequality (``a != b``)
* Bitwise OR (``a | b``)
* Power (``a ** b``)
* Subtraction (``a - b``)
* True Division (``a / b``)
* Bitwise XOR (``a ^ b``)
* Absolute Value (``abs(a)``)
* Inversion (``~a``)
* Negative Value (``neg(a)``)
* Positive Value (``pos(a)``)
* Indexing (``a[0]``)
.. note::
Some of these operations are only fully supported from Pytorch v1.4 and onwards, explicitly we found:
``add``, ``mul``, ``rmatmul``, ``rsub``, ``rmod``
****************
MetricCollection
****************
In many cases it is beneficial to evaluate the model output by multiple metrics.
In this case the ``MetricCollection`` class may come in handy. It accepts a sequence
of metrics and wraps these into a single callable metric class, with the same
interface as any other metric.
Example:
.. testcode::
from torchmetrics import MetricCollection
from torchmetrics.classification import MulticlassAccuracy, MulticlassPrecision, MulticlassRecall
target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
metric_collection = MetricCollection([
MulticlassAccuracy(num_classes=3, average="micro"),
MulticlassPrecision(num_classes=3, average="macro"),
MulticlassRecall(num_classes=3, average="macro")
])
print(metric_collection(preds, target))
.. testoutput::
:options: +NORMALIZE_WHITESPACE
{'MulticlassAccuracy': tensor(0.1250),
'MulticlassPrecision': tensor(0.0667),
'MulticlassRecall': tensor(0.1111)}
Similarly it can also reduce the amount of code required to log multiple metrics
inside your LightningModule. In most cases we just have to replace ``self.log`` with ``self.log_dict``.
.. testcode::
from torchmetrics import MetricCollection
from torchmetrics.classification import MulticlassAccuracy, MulticlassPrecision, MulticlassRecall
class MyModule(LightningModule):
def __init__(self, num_classes: int):
super().__init__()
metrics = MetricCollection([
MulticlassAccuracy(num_classes), MulticlassPrecision(num_classes), MulticlassRecall(num_classes)
])
self.train_metrics = metrics.clone(prefix='train_')
self.valid_metrics = metrics.clone(prefix='val_')
def training_step(self, batch, batch_idx):
logits = self(x)
# ...
output = self.train_metrics(logits, y)
# use log_dict instead of log
# metrics are logged with keys: train_Accuracy, train_Precision and train_Recall
self.log_dict(output)
def validation_step(self, batch, batch_idx):
logits = self(x)
# ...
self.valid_metrics.update(logits, y)
def on_validation_epoch_end(self):
# use log_dict instead of log
# metrics are logged with keys: val_Accuracy, val_Precision and val_Recall
output = self.valid_metrics.compute()
self.log_dict(output)
# remember to reset metrics at the end of the epoch
self.valid_metrics.reset()
.. note::
`MetricCollection` as default assumes that all the metrics in the collection
have the same call signature. If this is not the case, input that should be
given to different metrics can given as keyword arguments to the collection.
An additional advantage of using the ``MetricCollection`` object is that it will
automatically try to reduce the computations needed by finding groups of metrics
that share the same underlying metric state. If such a group of metrics is found
only one of them is actually updated and the updated state will be broadcasted to
the rest of the metrics within the group. In the example above, this will lead to
a 2-3x lower computational cost compared to disabling this feature in the case of
the validation metrics where only ``update`` is called (this feature does not work
in combination with ``forward``). However, this speedup comes with a fixed cost upfront,
where the state-groups have to be determined after the first update. In case the groups
are known beforehand, these can also be set manually to avoid this extra cost of the
dynamic search. See the *compute_groups* argument in the class docs below for more
information on this topic.
.. autoclass:: torchmetrics.MetricCollection
:exclude-members: update, compute, forward
***************
Metric wrappers
***************
In some cases it is beneficial to transform the output of one metric in some way or add additional logic. For this we
have implemented a few *Wrapper* metrics. Wrapper metrics always take another :class:`~torchmetrics.Metric` or (
:class:`~torchmetrics.MetricCollection`) as input and wraps it in some way. A good example of this is the
:class:`~torchmetrics.wrappers.ClasswiseWrapper` that allows for easy altering the output of certain classification
metrics to also include label information.
.. testcode::
from torchmetrics.classification import MulticlassAccuracy
from torchmetrics.wrappers import ClasswiseWrapper
# creating metrics
base_metric = MulticlassAccuracy(num_classes=3, average=None)
wrapped_metric = ClasswiseWrapper(base_metric, labels=["cat", "dog", "fish"])
# sample prediction and GT
target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
# showing the metric results
print(base_metric(preds, target)) # this returns a simple tensor without label info
print(wrapped_metric(preds, target)) # this returns a dict with label info
.. testoutput::
:options: +NORMALIZE_WHITESPACE
tensor([0.0000, 0.0000, 0.3333])
{'multiclassaccuracy_cat': tensor(0.),
'multiclassaccuracy_dog': tensor(0.),
'multiclassaccuracy_fish': tensor(0.3333)}
Another good example of wrappers is the :class:`~torchmetrics.wrappers.BootStrapper` that allows for easy bootstrapping
of metrics e.g. computation of confidence intervals by resampling of input data.
.. testcode::
from torchmetrics.classification import MulticlassAccuracy
from torchmetrics.wrappers import BootStrapper
# creating metrics
wrapped_metric = BootStrapper(MulticlassAccuracy(num_classes=3))
# sample prediction and GT
target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
# showing the metric results
print(wrapped_metric(preds, target)) # this returns a dict with label info
.. testoutput::
:options: +NORMALIZE_WHITESPACE
{'mean': tensor(0.1476), 'std': tensor(0.0613)}
You can see all implemented wrappers under the wrapper section of the API docs.
****************************
Module vs Functional Metrics
****************************
The functional metrics follow the simple paradigm input in, output out.
This means they don't provide any advanced mechanisms for syncing across DDP nodes or aggregation over batches.
They simply compute the metric value based on the given inputs.
Also, the integration within other parts of PyTorch Lightning will never be as tight as with the Module-based interface.
If you look for just computing the values, the functional metrics are the way to go.
However, if you are looking for the best integration and user experience, please consider also using the Module interface.
*****************************
Metrics and differentiability
*****************************
Metrics support backpropagation, if all computations involved in the metric calculation
are differentiable. All modular metric classes have the property ``is_differentiable`` that determines
if a metric is differentiable or not.
However, note that the cached state is detached from the computational
graph and cannot be back-propagated. Not doing this would mean storing the computational
graph for each update call, which can lead to out-of-memory errors.
In practice this means that:
.. code-block:: python
MyMetric.is_differentiable # returns True if metric is differentiable
metric = MyMetric()
val = metric(pred, target) # this value can be back-propagated
val = metric.compute() # this value cannot be back-propagated
A functional metric is differentiable if its corresponding modular metric is differentiable.
***************************************
Metrics and hyperparameter optimization
***************************************
If you want to directly optimize a metric it needs to support backpropagation (see section above).
However, if you are just interested in using a metric for hyperparameter tuning and are not sure
if the metric should be maximized or minimized, all modular metric classes have the ``higher_is_better``
property that can be used to determine this:
.. code-block:: python
# returns True because accuracy is optimal when it is maximized
torchmetrics.classification.Accuracy.higher_is_better
# returns False because the mean squared error is optimal when it is minimized
torchmetrics.MeanSquaredError.higher_is_better
.. _Metric kwargs:
************************
Advanced metric settings
************************
The following is a list of additional arguments that can be given to any metric class (in the ``**kwargs`` argument)
that will alter how metric states are stored and synced.
If you are running metrics on GPU and are encountering that you are running out of GPU VRAM then the following
argument can help:
- ``compute_on_cpu``: will automatically move the metric states to cpu after calling ``update``, making sure that
GPU memory is not filling up. The consequence will be that the ``compute`` method will be called on CPU instead
of GPU. Only applies to metric states that are lists.
- ``compute_with_cache``: This argument indicates if the result after calling the ``compute`` method should be cached.
By default this is ``True`` meaning that repeated calls to ``compute`` (with no change to the metric state in between)
does not recompute the metric but just returns the cache. By setting it to ``False`` the metric will be recomputed
every time ``compute`` is called, but it can also help clean up a bit of memory.
If you are running in a distributed environment, TorchMetrics will automatically take care of the distributed
synchronization for you. However, the following three keyword arguments can be given to any metric class for
further control over the distributed aggregation:
- ``sync_on_compute``: This argument is an ``bool`` that indicates if the metrics should automatically sync between
devices whenever the ``compute`` method is called. By default this is ``True``, but by setting this to ``False``
you can manually control when the synchronization happens.
- ``dist_sync_on_step``: This argument is ``bool`` that indicates if the metric should synchronize between
different devices every time ``forward`` is called. Setting this to ``True`` is in general not recommended
as synchronization is an expensive operation to do after each batch.
- ``process_group``: By default we synchronize across the *world* i.e. all processes being computed on. You
can provide an ``torch._C._distributed_c10d.ProcessGroup`` in this argument to specify exactly what
devices should be synchronized over.
- ``dist_sync_fn``: By default we use :func:`torch.distributed.all_gather` to perform the synchronization between
devices. Provide another callable function for this argument to perform custom distributed synchronization.
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/pages/implement.rst | .. _implement:
.. testsetup:: *
from typing import Optional, Sequence, Union
from torch import Tensor
#####################
Implementing a Metric
#####################
While we strive to include as many metrics as possible in ``torchmetrics``, we cannot include them all. Therefore, we
have made it easy to implement your own metric and possible contribute it to ``torchmetrics``. This page will guide
you through the process. If you afterwards are interested in contributing your metric to ``torchmetrics``, please
read the `contribution guidelines <https://lightning.ai/docs/torchmetrics/latest/generated/CONTRIBUTING.html>`_ and
see this :ref:`section <contributing metric>`.
**************
Base interface
**************
To implement your own custom metric, subclass the base :class:`~torchmetrics.Metric` class and implement the following
methods:
- ``__init__()``: Each state variable should be called using ``self.add_state(...)``.
- ``update()``: Any code needed to update the state given any inputs to the metric.
- ``compute()``: Computes a final value from the state of the metric.
We provide the remaining interface, such as ``reset()`` that will make sure to correctly reset all metric
states that have been added using ``add_state``. You should therefore not implement ``reset()`` yourself, only in rare
cases where not all the state variables should be reset to their default value. Adding metric states with ``add_state``
will make sure that states are correctly synchronized in distributed settings (DDP). To see how metric states are
synchronized across distributed processes, refer to :meth:`~torchmetrics.Metric.add_state()` docs from the base
:class:`~torchmetrics.Metric` class.
Below is a basic implementation of a custom accuracy metric. In the ``__init__`` method we add the metric states
``correct`` and ``total``, which will be used to accumulate the number of correct predictions and the total number
of predictions, respectively. In the ``update`` method we update the metric states based on the inputs to the metric.
Finally, in the ``compute`` method we compute the final metric value based on the metric states.
.. testcode::
from torchmetrics import Metric
class MyAccuracy(Metric):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.add_state("correct", default=torch.tensor(0), dist_reduce_fx="sum")
self.add_state("total", default=torch.tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
preds, target = self._input_format(preds, target)
if preds.shape != target.shape:
raise ValueError("preds and target must have the same shape")
self.correct += torch.sum(preds == target)
self.total += target.numel()
def compute(self) -> Tensor:
return self.correct.float() / self.total
A few important things to note:
* The ``dist_reduce_fx`` argument to ``add_state`` is used to specify how the metric states should be reduced between
batches in distributed settings. In this case we use ``"sum"`` to sum the metric states across batches. A couple of
build in options are available: ``"sum"``, ``"mean"``, ``"cat"``, ``"min"`` or ``"max"``, but a custom reduction is
also supported.
* In ``update`` we do not return anything but instead update the metric states in-place.
* In ``compute`` when running in distributed mode, the states would have been synced before the compute method is
called. Thus ``self.correct`` and ``self.total`` will contain the sum of the metric states across all processes.
************************
Working with list states
************************
When initializing metric states with ``add_state``, the ``default`` argument can either be a single tensor (as in the
example above) or an empty list. Most metric will only require a single tensor to accumulate the metric states, but
for some metrics that need access to the individual batch states, it can be useful to use a list of tensors. In the
following example we show how to implement Spearman correlation, which requires access to the individual batch states
because we need to calculate the rank of the predictions and targets.
.. testcode::
from torchmetrics import Metric
from torchmetrics.utilities import dim_zero_cat
class MySpearmanCorrCoef(Metric):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.add_state("preds", default=[], dist_reduce_fx="cat")
self.add_state("target", default=[], dist_reduce_fx="cat")
def update(self, preds: Tensor, target: Tensor) -> None:
self.preds.append(preds)
self.target.append(target)
def compute(self):
# parse inputs
preds = dim_zero_cat(preds)
target = dim_zero_cat(target)
# some intermediate computation...
r_preds, r_target = _rank_data(preds), _rank_dat(target)
preds_diff = r_preds - r_preds.mean(0)
target_diff = r_target - r_target.mean(0)
cov = (preds_diff * target_diff).mean(0)
preds_std = torch.sqrt((preds_diff * preds_diff).mean(0))
target_std = torch.sqrt((target_diff * target_diff).mean(0))
# finalize the computations
corrcoef = cov / (preds_std * target_std + eps)
return torch.clamp(corrcoef, -1.0, 1.0)
A few important things to note for this example:
* When working with list states, the ``dist_reduce_fx`` argument to ``add_state`` should be set to ``"cat"`` to
concatenate the list of tensors across batches.
* When working with list states, The ``update(...)`` method should append the batch states to the list.
* In the the ``compute`` method the list states behave a bit differently dependeding on weather you are running in
distributed mode or not. In non-distributed mode the list states will be a list of tensors, while in distributed mode
the list have already been concatenated into a single tensor. For this reason, we recommend always using the
``dim_zero_cat`` helper function which will standardize the list states to be a single concatenate tensor regardless
of the mode.
*****************
Metric attributes
*****************
When done implementing your own metric, there are a few properties and attributes that you may want to set to add
additional functionality. The three attributes to consider are: ``is_differentiable``, ``higher_is_better`` and
``full_state_update``. Note that none of them are strictly required to be set for the metric to work.
.. testcode::
from torchmetrics import Metric
class MyMetric(Metric):
# Set to True if the metric is differentiable else set to False
is_differentiable: Optional[bool] = None
# Set to True if the metric reaches it optimal value when the metric is maximized.
# Set to False if it when the metric is minimized.
higher_is_better: Optional[bool] = True
# Set to True if the metric during 'update' requires access to the global metric
# state for its calculations. If not, setting this to False indicates that all
# batch states are independent and we will optimize the runtime of 'forward'
full_state_update: bool = True
**************
Plot interface
**************
From torchmetrics v1.0.0 onwards, we also support plotting of metrics through the ``.plot()`` method. By default this method
will raise `NotImplementedError` but can be implemented by the user to provide a custom plot for the metric.
For any metrics that returns a simple scalar tensor, or a dict of scalar tensors the internal `._plot` method can be
used, that provides the common plotting functionality for most metrics in torchmetrics.
.. testcode::
from torchmetrics import Metric
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
class MyMetric(Metric):
# set these attributes if you want to use the internal ._plot method
# bounds are automatically added to the generated plot
plot_lower_bound: Optional[float] = None
plot_upper_bound: Optional[float] = None
def plot(
self, val: Optional[Union[Tensor, Sequence[Tensor]]] = None, ax: Optional[_AX_TYPE] = None
) -> _PLOT_OUT_TYPE:
return self._plot(val, ax)
If the metric returns a more complex output, a custom implementation of the `plot` method is required. For more details
on the plotting API, see the this :ref:`page <plotting>` . In addti
*******************************
Internal implementation details
*******************************
This section briefly describes how metrics work internally. We encourage looking at the source code for more info.
Internally, TorchMetrics wraps the user defined ``update()`` and ``compute()`` method. We do this to automatically
synchronize and reduce metric states across multiple devices. More precisely, calling ``update()`` does the
following internally:
1. Clears computed cache.
2. Calls user-defined ``update()``.
Similarly, calling ``compute()`` does the following internally:
1. Syncs metric states between processes.
2. Reduce gathered metric states.
3. Calls the user defined ``compute()`` method on the gathered metric states.
4. Cache computed result.
From a user's standpoint this has one important side-effect: computed results are cached. This means that no
matter how many times ``compute`` is called after one and another, it will continue to return the same result.
The cache is first emptied on the next call to ``update``.
``forward`` serves the dual purpose of both returning the metric on the current data and updating the internal
metric state for accumulating over multiple batches. The ``forward()`` method achieves this by combining calls
to ``update``, ``compute`` and ``reset``. Depending on the class property ``full_state_update``, ``forward``
can behave in two ways:
1. If ``full_state_update`` is ``True`` it indicates that the metric during ``update`` requires access to the full
metric state and we therefore need to do two calls to ``update`` to secure that the metric is calculated correctly
1. Calls ``update()`` to update the global metric state (for accumulation over multiple batches)
2. Caches the global state.
3. Calls ``reset()`` to clear global metric state.
4. Calls ``update()`` to update local metric state.
5. Calls ``compute()`` to calculate metric for current batch.
6. Restores the global state.
2. If ``full_state_update`` is ``False`` (default) the metric state of one batch is completely independent of the state
of other batches, which means that we only need to call ``update`` once.
1. Caches the global state.
2. Calls ``reset`` the metric to its default state
3. Calls ``update`` to update the state with local batch statistics
4. Calls ``compute`` to calculate the metric for the current batch
5. Reduce the global state and batch state into a single state that becomes the new global state
If implementing your own metric, we recommend trying out the metric with ``full_state_update`` class property set to
both ``True`` and ``False``. If the results are equal, then setting it to ``False`` will usually give the best
performance.
.. autoclass:: torchmetrics.Metric
:noindex:
:members:
.. _contributing metric:
****************************************
Contributing your metric to TorchMetrics
****************************************
Wanting to contribute the metric you have implemented? Great, we are always open to adding more metrics to ``torchmetrics``
as long as they serve a general purpose. However, to keep all our metrics consistent we request that the implementation
and tests gets formatted in the following way:
1. Start by reading our `contribution guidelines <https://lightning.ai/docs/torchmetrics/latest/generated/CONTRIBUTING.html>`_.
2. First implement the functional backend. This takes cares of all the logic that goes into the metric. The code should
be put into a single file placed under ``src/torchmetrics/functional/"domain"/"new_metric".py`` where ``domain`` is the type of
metric (classification, regression, text etc.) and ``new_metric`` is the name of the metric. In this file, there should be the
following three functions:
1. ``_new_metric_update(...)``: everything that has to do with type/shape checking and all logic required before distributed syncing need to go here.
2. ``_new_metric_compute(...)``: all remaining logic.
3. ``new_metric(...)``: essentially wraps the ``_update`` and ``_compute`` private functions into one public function that
makes up the functional interface for the metric.
.. note::
The `functional mean squared error <https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/functional/regression/mse.py>`_
metric is a great example of this division of logic.
3. In a corresponding file placed in ``src/torchmetrics/"domain"/"new_metric".py`` create the module interface:
1. Create a new module metric by subclassing ``torchmetrics.Metric``.
2. In the ``__init__`` of the module call ``self.add_state`` for as many metric states are needed for the metric to
proper accumulate metric statistics.
3. The module interface should essentially call the private ``_new_metric_update(...)`` in its `update` method and similarly the
``_new_metric_compute(...)`` function in its ``compute``. No logic should really be implemented in the module interface.
We do this to not have duplicate code to maintain.
.. note::
The module `MeanSquaredError <https://github.com/Lightning-AI/torchmetrics/blob/master/src/torchmetrics/regression/mse.py>`_
metric that corresponds to the above functional example showcases these steps.
4. Remember to add binding to the different relevant ``__init__`` files.
5. Testing is key to keeping ``torchmetrics`` trustworthy. This is why we have a very rigid testing protocol. This means
that we in most cases require the metric to be tested against some other common framework (``sklearn``, ``scipy`` etc).
1. Create a testing file in ``tests/unittests/"domain"/test_"new_metric".py``. Only one file is needed as it is intended to test
both the functional and module interface.
2. In that file, start by defining a number of test inputs that your metric should be evaluated on.
3. Create a testclass ``class NewMetric(MetricTester)`` that inherits from ``tests.helpers.testers.MetricTester``.
This testclass should essentially implement the ``test_"new_metric"_class`` and ``test_"new_metric"_fn`` methods that
respectively tests the module interface and the functional interface.
4. The testclass should be parameterized (using ``@pytest.mark.parametrize``) by the different test inputs defined initially.
Additionally, the ``test_"new_metric"_class`` method should also be parameterized with an ``ddp`` parameter such that it gets
tested in a distributed setting. If your metric has additional parameters, then make sure to also parameterize these
such that different combinations of inputs and parameters gets tested.
5. (optional) If your metric raises any exception, please add tests that showcase this.
.. note::
The `test file for MSE <https://github.com/Lightning-AI/torchmetrics/blob/master/tests/unittests/regression/test_mean_error.py>`_
metric shows how to implement such tests.
If you only can figure out part of the steps, do not fear to send a PR. We will much rather receive working
metrics that are not formatted exactly like our codebase, than not receiving any. Formatting can always be applied.
We will gladly guide and/or help implement the remaining :]
| 0 |
public_repos/torchmetrics/docs/source | public_repos/torchmetrics/docs/source/pages/plotting.rst | .. _plotting:
.. testsetup:: *
import torch
import matplotlib
import matplotlib.pyplot as plt
import torchmetrics
########
Plotting
########
.. note::
The visualization/plotting interface of Torchmetrics requires ``matplotlib`` to be installed. Install with either
``pip install matplotlib`` or ``pip install 'torchmetrics[visual]'``. If the latter option is chosen the
`Scienceplot package <https://github.com/garrettj403/SciencePlots>`_ is also installed and all plots in
Torchmetrics will default to using that style.
Torchmetrics comes with built-in support for quick visualization of your metrics, by simply using the ``.plot`` method
that all modular metrics implement. This method provides a consistent interface for basic plotting of all metrics.
.. code-block:: python
metric = AnyMetricYouLike()
for _ in range(num_updates):
metric.update(preds[i], target[i])
fig, ax = metric.plot()
``.plot`` will always return two objects: ``fig`` is an instance of :class:`~matplotlib.figure.Figure` which contains
figure level attributes and `ax` is an instance of :class:`~matplotlib.axes.Axes` that contains all the elements of the
plot. These two objects allow to change attributes of the plot after it is created. For example, if you want to make
the fontsize of the x-axis a bit bigger and give the figure a nice title and finally save it on the above example, it
could be do like this:
.. code-block:: python
ax.set_fontsize(fs=20)
fig.set_title("This is a nice plot")
fig.savefig("my_awesome_plot.png")
If you want to include a Torchmetrics plot in a bigger figure that has subfigures and subaxes, all ``.plot`` methods
support an optional `ax` argument where you can pass in the subaxes you want the plot to be inserted into:
.. code-block:: python
# combine plotting of two metrics into one figure
fig, ax = plt.subplots(nrows=1, ncols=2)
metric1 = Metric1()
metric2 = Metric2()
for _ in range(num_updates):
metric1.update(preds[i], target[i])
metric2.update(preds[i], target[i])
metric1.plot(ax=ax[0])
metric2.plot(ax=ax[1])
**********************
Plotting a single step
**********************
At the most basic level the ``.plot`` method can be used to plot the value from a single step. This can be done in two
ways:
* Either ``.plot`` method is called with no input, and internally ``metric.compute()`` is called and that value is plotted
* ``.plot`` is called on a single returned value by the metric, for example from ``metric.forward()``
In both cases it will generate a plot like this (Accuracy as an example):
.. code-block:: python
metric = torchmetrics.Accuracy(task="binary")
for _ in range(num_updates):
metric.update(torch.rand(10,), torch.randint(2, (10,)))
fig, ax = metric.plot()
.. plot:: pyplots/binary_accuracy.py
:scale: 100
:include-source: false
A single point plot is not that informative in itself, but if available we will try to include additional information
such as the lower and upper bounds the particular metric can take and if the metric should be minimized or maximized
to be optimal. This is true for all metrics that return a scalar tensor.
Some metrics return multiple values (such as an tensor with multiple elements or a dict of scalar tensors), and in
that case calling ``.plot`` will return a figure similar to this:
.. code-block:: python
metric = torchmetrics.Accuracy(task="multiclass", num_classes=3, average=None)
for _ in range(num_updates):
metric.update(torch.randint(3, (10,)), torch.randint(3, (10,)))
fig, ax = metric.plot()
.. plot:: pyplots/multiclass_accuracy.py
:scale: 100
:include-source: false
Here, each element is assumed to be an independent metric and plotted as its own point for comparing. The above is true
for all metrics that return a scalar tensor, but if the metric returns a tensor with multiple elements then the
``.plot`` method will return a specialized plot for that particular metric. Take for example the ``ConfusionMatrix``
metric:
.. code-block:: python
metric = torchmetrics.ConfusionMatrix(task="multiclass", num_classes=3)
for _ in range(num_updates):
metric.update(torch.randint(3, (10,)), torch.randint(3, (10,)))
fig, ax = metric.plot()
.. plot:: pyplots//confusion_matrix.py
:scale: 100
:include-source: false
If you prefer to use the functional interface of Torchmetrics, you can also plot the values returned by the functional.
However, you would still need to initialize the corresponding metric class to get the information about the metric:
.. code-block:: python
plot_class = torchmetrics.Accuracy(task="multiclass", num_classes=3)
value = torchmetrics.functional.accuracy(
torch.randint(3, (10,)), torch.randint(3, (10,)), num_classes=3
)
fig, ax = plot_class.plot(value)
********************
Plotting multi steps
********************
In the above examples we have only plotted a single step/single value, but it is also possible to plot multiple steps
from the same metric. This is often the case when training a machine learning model, where you are tracking one or
multiple metrics that you want to plot as they are changing over time. This can be done by providing a sequence of outputs from
any metric, computed using ``metric.forward`` or ``metric.compute``. For example, if we want to plot the accuracy of
a model over time, we could do it like this:
.. code-block:: python
metric = torchmetrics.Accuracy(task="binary")
values = [ ]
for step in range(num_steps):
for _ in range(num_updates):
metric.update(preds(step), target(step))
values.append(metric.compute()) # save value
metric.reset()
fig, ax = metric.plot(values)
.. plot:: pyplots/binary_accuracy_multistep.py
:scale: 100
:include-source: false
Do note that metrics that do not return simple scalar tensors, such as `ConfusionMatrix`, `ROC` that have specialized
visualization does not support plotting multiple steps, out of the box and the user needs to manually plot the values
for each step.
********************************
Plotting a collection of metrics
********************************
``MetricCollection`` also supports `.plot` method and by default it works by just returning a collection of plots for
all its members. Thus, instead of returning a single (fig, ax) pair, calling `.plot` method of ``MetricCollection`` will
return a sequence of such pairs, one for each member in the collection. In the following example we are forming a
collection of binary classification metrics and redirecting the output of ``.plot`` to different subplots:
.. code-block:: python
collection = torchmetrics.MetricCollection(
torchmetrics.Accuracy(task="binary"),
torchmetrics.Recall(task="binary"),
torchmetrics.Precision(task="binary"),
)
fig, ax = plt.subplots(nrows=1, ncols=3)
values = [ ]
for step in range(num_steps):
for _ in range(num_updates):
collection.update(preds(step), target(step))
values.append(collection.compute())
collection.reset()
collection.plot(val=values, ax=ax)
.. plot:: pyplots/binary_accuracy_multistep.py
:scale: 100
:include-source: false
However, the ``plot`` method of ``MetricCollection`` also supports an additional argument called ``together`` that will
automatically try to plot all the metrics in the collection together in the same plot (with appropriate labels). This
is only possible if all the metrics in the collection return a scalar tensor.
.. code-block:: python
collection = torchmetrics.MetricCollection(
torchmetrics.Accuracy(task="binary"),
torchmetrics.Recall(task="binary"),
torchmetrics.Precision(task="binary"),
)
values = [ ]
fig, ax = plt.subplots(figsize=(6.8, 4.8))
for step in range(num_steps):
for _ in range(num_updates):
collection.update(preds(step), target(step))
values.append(collection.compute())
collection.reset()
collection.plot(val=values, together=True)
.. plot:: pyplots/collection_binary_together.py
:scale: 100
:include-source: false
***************
Advance example
***************
In the following we are going to show how to use the ``.plot`` method to create a more advanced plot. We are going to
combine the functionality of several metrics using ``MetricCollection`` and plot them together. In addition we are going
to rely on ``MetricTracker`` to keep track of the metrics over multiple steps.
.. code-block:: python
# Define collection that is a mix of metrics that return a scalar tensors and not
confmat = torchmetrics.ConfusionMatrix(task="binary")
roc = torchmetrics.ROC(task="binary")
collection = torchmetrics.MetricCollection(
torchmetrics.Accuracy(task="binary"),
torchmetrics.Recall(task="binary"),
torchmetrics.Precision(task="binary"),
confmat,
roc,
)
# Define tracker over the collection to easy keep track of the metrics over multiple steps
tracker = torchmetrics.wrappers.MetricTracker(collection)
# Run "training" loop
for step in range(num_steps):
tracker.increment()
for _ in range(N):
tracker.update(preds(step), target(step))
# Extract all metrics from all steps
all_results = tracker.compute_all()
# Construct a single figure with appropriate layout for all metrics
fig = plt.figure(layout="constrained")
ax1 = plt.subplot(2, 2, 1)
ax2 = plt.subplot(2, 2, 2)
ax3 = plt.subplot(2, 2, (3, 4))
# ConfusionMatrix and ROC we just plot the last step, notice how we call the plot method of those metrics
confmat.plot(val=all_results[-1]['BinaryConfusionMatrix'], ax=ax1)
roc.plot(all_results[-1]["BinaryROC"], ax=ax2)
# For the remaining we plot the full history, but we need to extract the scalar values from the results
scalar_results = [
{k: v for k, v in ar.items() if isinstance(v, torch.Tensor) and v.numel() == 1} for ar in all_results
]
tracker.plot(val=scalar_results, ax=ax3)
.. plot:: pyplots/tracker_binary.py
:scale: 100
:include-source: false
| 0 |
public_repos/torchmetrics/docs | public_repos/torchmetrics/docs/paper_JOSS/paper.md | ---
title: TorchMetrics - Measuring Reproducibility in PyTorch
tags:
- python
- deep learning
- pytorch
authors:
- name: Nicki Skafte Detlefsen
affiliation: '1,2'
orcid: 0000-0002-8133-682X
- name: Jiri Borovec
affiliation: '1'
orcid: 0000-0001-7437-824X
- name: Justus Schock
affiliation: '1,3'
orcid: 0000-0003-0512-3053
- name: Ananya Harsh Jha
affiliation: '1'
- name: Teddy Koker
affiliation: '1'
- name: Luca Di Liello
affiliation: '4'
- name: Daniel Stancl
affiliation: '5'
- name: Changsheng Quan
affiliation: '6'
- name: Maxim Grechkin
affiliation: '7'
- name: William Falcon
affiliation: '1,8'
affiliations:
- name: Grid AI Labs
index: 1
- name: Technical University of Denmark
index: 2
- name: University Hospital Düsseldorf
index: 3
- name: University of Trento
index: 4
- name: Charles University
index: 5
- name: Zhejiang University
index: 6
- name: Independent Researcher
index: 7
- name: New York University
index: 8
date: 08 Dec 2021
bibliography: paper.bib
---
# Summary
A main problem with reproducing machine learning publications is the variance of metric implementations across papers. A lack of standardization leads to different behavior in mechanisms such as checkpointing, learning rate schedulers or early stopping, that will influence the reported results. For example, a complex metric such as Fréchet inception distance (FID) for synthetic image quality evaluation [@fid] will differ based on the specific interpolation method used.
There have been a few attempts at tackling the reproducibility issues. Papers With Code [@papers_with_code] links research code with its corresponding paper. Similarly, arXiv [@arxiv] recently added a code and data section that links both official and community code to papers. However, these methods rely on the paper code to be made publicly accessible which is not always possible. Our approach is to provide the de-facto reference implementation for metrics. This approach enables proprietary work to still be comparable as long as they’ve used our reference implementations.
We introduce TorchMetrics, a general-purpose metrics package that covers a wide variety of tasks and domains used in the machine learning community. TorchMetrics provides standard classification and regression metrics; and domain-specific metrics for audio, computer vision, natural language processing, and information retrieval. Our process for adding a new metric is as follows, first we integrate a well-tested and established third-party library. Once we’ve verified the implementations and written tests for them, we re-implement them in native PyTorch [@pytorch] to enable hardware acceleration and remove any bottlenecks in inter-device transfer.
# Statement of need
Currently, there is no standard, widely-adopted metrics library for native PyTorch. Some native PyTorch libraries support domain-specific metrics such as Transformers [@transformers] for calculating NLP-specific metrics. However, no library exists that covers multiple domains. PyTorch users, therefore, often rely on non-PyTorch packages such as Scikit-learn [@scikit_learn] for computing even simple metrics such as accuracy, F1, or AUROC metrics.
However, while Scikit-learn is considered the gold standard for computing metrics in regression and classification, it relies on the core assumption that all predictions and targets are available simultaneously. This contradicts the typical workflow in a modern deep learning training/evaluation loop where data comes in batches. Therefore, the metric needs to be calculated in an online fashion. It is important to note that, in general, it is not possible to calculate a global metric as its average or sum of the metric calculated per batch.
TorchMetrics solves this problem by introducing stateful metrics that can calculate metric values on a stream of data alongside the classical functional and stateless metrics provided by other packages like Scikit-learn. We do this with an effortless `update` and `compute` interface, well known from packages such as Keras [@keras]. The `update` function takes in a batch of predictions and targets and updates the internal state. For example, for a metric such as accuracy, the internal states are simply the number of correctly classified samples and the total observed number of samples. When all batches have been passed to the `update` method, the `compute` method can get the accumulated accuracy over all the batches. In addition to `update` and `compute`, each metric also has a `forward` method (as any other `torch.nn.Module`) that can be used to both get the metric on the current batch of data and accumulate global state. This enables the user to get fine-grained info about the metric on the individual batch and the global metric of how well their model is doing.
```python
# Minimal example showcasing the TorchMetrics interface
import torch
from torch import tensor, Tensor
# base class all modular metrics inherit from
from torchmetrics import Metric
class Accuracy(Metric):
def __init__(self):
super().__init__()
# `self.add_state` defines the states of the metric
# that should be accumulated and will automatically
# be synchronized between devices
self.add_state("correct", default=tensor(0), dist_reduce_fx="sum")
self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
def update(self, preds: Tensor, target: Tensor) -> None:
# update takes `preds` and `target` and accumulate the current
# stream of data into the global states for later
self.correct += torch.sum(preds == target)
self.total += target.numel()
def compute(self) -> Tensor:
# compute takes the accumulated states
# and returns the final metric value
return self.correct / self.total
```
Another core feature of TorchMetrics is its ability to scale to multiple devices seamlessly. Modern deep learning models are often trained on hundreds of devices such as GPUs or TPUs (see @large_example1; @large_example2 for examples). This scale introduces the need to synchronize metrics across machines to get the correct value during training and evaluation. In distributed environments, TorchMetrics automatically accumulates across devices before reporting the calculated metric to the user.
In addition to stateful metrics (called modular metrics in TorchMetrics), we also support a functional interface that works similar to Scikit-learn. This interface provides simple Python functions that take as input PyTorch Tensors and return the corresponding metric as a PyTorch Tensor. These can be used when metrics are evaluated on single devices, and no accumulation is needed, making them very fast to compute.
TorchMetrics exhibits high test coverage on the various configurations, including all three major OS platforms (Linux, macOS, and Windows), and various Python, CUDA, and PyTorch versions. We test both minimum and latest package requirements for all combinations of OS and Python versions and include additional tests for each PyTorch version from 1.3 up to future development versions. On every pull request and merge to master, we run a full test suite. All standard tests run on CPU. In addition, we run all tests on a multi-GPU setting which reflects realistic Deep Learning workloads. For usability, we have auto-generated HTML documentation (hosted at [readthedocs](https://torchmetrics.readthedocs.io/en/stable/)) from the source code which updates in real-time with new merged pull requests.
TorchMetrics is released under the Apache 2.0 license. The source code is available at https://github.com/Lightning-AI/torchmetrics.
# Acknowledgement
The TorchMetrics team thanks Thomas Chaton, Ethan Harris, Carlos Mocholí, Sean Narenthiran, Adrian Wälchli, and Ananth Subramaniam for contributing ideas, participating in discussions on API design, and completing Pull Request reviews. We also thank all of our open-source contributors for reporting and resolving issues with this package. We are grateful to the PyTorch Lightning team for their ongoing and dedicated support of this project, and Grid.ai for providing computing resources and cloud credits needed to run our Continuous Integrations.
# References
| 0 |
public_repos/torchmetrics/docs | public_repos/torchmetrics/docs/paper_JOSS/paper.bib | @misc{reproducibility,
title={Enabling End-To-End Machine Learning Replicability: A Case Study in Educational Data Mining},
author={Josh Gardner and Yuming Yang and Ryan Baker and Christopher Brooks},
year={2018},
eprint={1806.05208},
archivePrefix={arXiv},
primaryClass={cs.CY}
}
@inproceedings{fid,
title={GANs Trained by a Two Time-Scale Update Rule Converge to a Local {N}ash Equilibrium},
volume = {30},
url = {https://proceedings.neurips.cc/paper/2017/hash/8a1d694707eb0fefe65871369074926d-Abstract.html},
booktitle = {Advances in Neural Information Processing Systems},
publisher = {Curran Associates, Inc.},
author = {Heusel, Martin and Ramsauer, Hubert and Unterthiner, Thomas and Nessler, Bernhard and Hochreiter, Sepp},
pages={6629--6640},
year = {2017},
}
@misc{papers_with_code,
title = {Papers With Code},
author = {},
howpublished = {\url{https://paperswithcode.com/}},
note = {Accessed: 2021-12-01}
}
@misc{arxiv,
title = {Arxiv},
author = {},
howpublished = {\url{https://arxiv.org/}},
note = {Accessed: 2021-12-16}
}
@incollection{pytorch,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.},
url={http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}
@inproceedings{transformers,
title = "Transformers: State-of-the-Art Natural Language Processing",
author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
pages = "38--45"
}
@article{scikit_learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
@misc{keras,
title={Keras},
author={Chollet, François and others},
year={2015},
publisher={GitHub},
howpublished={\url{https://github.com/fchollet/keras}},
}
@misc{large_example1,
title={Scaling Vision Transformers},
author={Xiaohua Zhai and Alexander Kolesnikov and Neil Houlsby and Lucas Beyer},
year={2021},
eprint={2106.04560},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{large_example2,
title={RoBERTa: A Robustly Optimized BERT Pretraining Approach},
author={Yinhan Liu and Myle Ott and Naman Goyal and Jingfei Du and Mandar Joshi and Danqi Chen and Omer Levy and Mike Lewis and Luke Zettlemoyer and Veselin Stoyanov},
year={2019},
eprint={1907.11692},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
| 0 |
public_repos | public_repos/twitter-finetune/app.py | import langchain
import streamlit as st
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.schema.output_parser import StrOutputParser
from langchain.callbacks.tracers.langchain import wait_for_all_tracers
from langchain.callbacks.tracers.run_collector import RunCollectorCallbackHandler
from langchain.schema.runnable import RunnableConfig
from langsmith import Client
from streamlit_feedback import streamlit_feedback
client = Client()
if "run_id" not in st.session_state:
st.session_state.run_id = None
run_collector = RunCollectorCallbackHandler()
runnable_config = RunnableConfig(
callbacks=[run_collector],
tags=["Streamlit Chat"],
)
normal_chain = (
ChatPromptTemplate.from_messages([("system", "write a tweet about {topic} in the style of Elon Musk") ])
| ChatOpenAI()
| StrOutputParser()
)
chain = (
ChatPromptTemplate.from_messages([("system", "write a tweet about {topic}") ])
| ChatOpenAI(model="ft:gpt-3.5-turbo-0613:langchain::7qqjIosa")
| StrOutputParser()
)
def generate_tweet_normal(topic):
result = normal_chain.invoke({"topic": topic})
wait_for_all_tracers()
return result
def generate_tweet(topic):
result = chain.invoke({"topic": topic}, config=runnable_config)
run = run_collector.traced_runs[0]
run_collector.traced_runs = []
st.session_state.run_id = run.id
wait_for_all_tracers()
return result
col1, col2 = st.columns([1, 6]) # Adjust the ratio for desired layout
# Display the smaller image in the first column
col1.image("elon.jpeg") # Adjust width as needed
# Display the title in the second column
col2.title("Elon Musk Tweet Generator")
st.info("This generator was finetuned on tweets by Elon Musk to imitate his style. Source code [here](https://github.com/langchain-ai/twitter-finetune)\n\nTwo tweets will be generated: one using a finetuned model, one useing a prompted model. Afterwards, you can provide feedback about whether the finetuned model performed better!")
topic = st.text_input("Enter a topic:")
if 'show_tweets' not in st.session_state:
st.session_state.show_tweets = None
if st.button("Generate Tweets"):
if topic:
col3, col4 = st.columns([6, 6])
tweet = generate_tweet(topic)
col3.markdown("### Finetuned Tweet:")
col3.write(f"🐦: {tweet}")
col3.markdown("---") # Add a horizontal line for separation
feedback = streamlit_feedback(
feedback_type="thumbs",
key=f"feedback_{st.session_state.run_id}",
align="flex-start"
)
scores = {"👍": 1, "👎": 0}
if feedback:
score = scores[feedback["score"]]
feedback = client.create_feedback(st.session_state.run_id, "user_score", score=score)
st.session_state.feedback = {"feedback_id": str(feedback.id), "score": score}
tweet = generate_tweet_normal(topic)
col4.markdown("### Prompted Tweet:")
col4.write(f"🐦: {tweet}")
col4.markdown("---") # Add a horizontal line for separation
else:
st.warning("Please enter a topic before generating a tweet.")
| 0 |
public_repos | public_repos/twitter-finetune/requirements.txt | openai
langchain
streamlit
streamlit_feedback
| 0 |
public_repos | public_repos/twitter-finetune/README.md | # Twitter Finetune
This repo shows how to finetune GPT-3.5-Turbo on tweets. Specifically, Elon Musk's tweets.
In this example, data is first exported from Twitter using [Apify](https://apify.com/).
A copy of this data can be found in [dataset_twitter-scraper_2023-08-23_22-13-19-740.json](dataset_twitter-scraper_2023-08-23_22-13-19-740.json).
This data is then loaded to a format with which it can be used to finetune a GPT-3.5-Turbo model, and is then used to do exactly that. This can be done by running `python ingest.py`.
A Streamlit app is this created to compare this finetuned model to a prompted GPT-3.5-Turbo model.
This can be run with `streamlit run app.py`.
Access the final app hosted on Streamlit [here](https://elon-twitter-clone.streamlit.app/).
| 0 |
public_repos | public_repos/twitter-finetune/dataset_twitter-scraper_2023-08-23_22-13-19-740.json | [{
"username": "elonmusk",
"user_id": "44196397",
"id": "1519480761749016577",
"conversation_id": "1519480761749016577",
"full_text": "Next I’m buying Coca-Cola to put the cocaine back in",
"reply_count": 187291,
"retweet_count": 648962,
"favorite_count": 4596262,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [],
"url": "https://twitter.com/elonmusk/status/1519480761749016577",
"created_at": "2022-04-28T00:56:58.000Z",
"quote_count": 171980,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1518623997054918657",
"conversation_id": "1518623997054918657",
"full_text": "I hope that even my worst critics remain on Twitter, because that is what free speech means",
"reply_count": 174468,
"retweet_count": 351409,
"favorite_count": 3105543,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [],
"url": "https://twitter.com/elonmusk/status/1518623997054918657",
"created_at": "2022-04-25T16:12:30.000Z",
"quote_count": 70717,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1519495072802390016",
"conversation_id": "1519495072802390016",
"full_text": "Let’s make Twitter maximum fun!",
"reply_count": 110542,
"retweet_count": 184310,
"favorite_count": 2542681,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [],
"url": "https://twitter.com/elonmusk/status/1519495072802390016",
"created_at": "2022-04-28T01:53:50.000Z",
"quote_count": 34654,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1518677066325053441",
"conversation_id": "1518677066325053441",
"full_text": "🚀💫♥️ Yesss!!! ♥️💫🚀 https://t.co/0T9HzUHuh6",
"reply_count": 145150,
"retweet_count": 330889,
"favorite_count": 2505986,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [
{
"media_url": "https://pbs.twimg.com/media/FRNsuSFWUAUW6aP.jpg",
"type": "photo"
}
],
"url": "https://twitter.com/elonmusk/status/1518677066325053441",
"created_at": "2022-04-25T19:43:22.000Z",
"quote_count": 61362,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1519495982723084290",
"conversation_id": "1519495982723084290",
"full_text": "Listen, I can’t do miracles ok https://t.co/z7dvLMUXy8",
"reply_count": 74894,
"retweet_count": 202182,
"favorite_count": 2473727,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [
{
"media_url": "https://pbs.twimg.com/media/FRZViwWX0AMsqQ1.jpg",
"type": "photo"
}
],
"url": "https://twitter.com/elonmusk/status/1519495982723084290",
"created_at": "2022-04-28T01:57:27.000Z",
"quote_count": 25525,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1585841080431321088",
"conversation_id": "1585841080431321088",
"full_text": "the bird is freed",
"reply_count": 137792,
"retweet_count": 330476,
"favorite_count": 2370568,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [],
"url": "https://twitter.com/elonmusk/status/1585841080431321088",
"created_at": "2022-10-28T03:49:11.000Z",
"quote_count": 52481,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1586104694421659648",
"conversation_id": "1586104694421659648",
"full_text": "Comedy is now legal on Twitter",
"reply_count": 87869,
"retweet_count": 236840,
"favorite_count": 2274201,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [],
"url": "https://twitter.com/elonmusk/status/1586104694421659648",
"created_at": "2022-10-28T21:16:42.000Z",
"quote_count": 39559,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1593459801966538755",
"conversation_id": "1593459801966538755",
"full_text": "https://t.co/rbwbsLA1ZG",
"reply_count": 67716,
"retweet_count": 218714,
"favorite_count": 2030318,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [
{
"media_url": "https://pbs.twimg.com/media/Fh0bPd7VQAAU31D.jpg",
"type": "photo"
}
],
"url": "https://twitter.com/elonmusk/status/1593459801966538755",
"created_at": "2022-11-18T04:23:16.000Z",
"quote_count": 165205,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1523465632502906880",
"conversation_id": "1523465632502906880",
"full_text": "If I die under mysterious circumstances, it’s been nice knowin ya",
"reply_count": 142816,
"retweet_count": 164576,
"favorite_count": 1814691,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [],
"url": "https://twitter.com/elonmusk/status/1523465632502906880",
"created_at": "2022-05-09T00:51:26.000Z",
"quote_count": 40125,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1587894226695884800",
"conversation_id": "1587894226695884800",
"full_text": "https://t.co/kGncG7Hs3M",
"reply_count": 71915,
"retweet_count": 167091,
"favorite_count": 1796047,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [
{
"media_url": "https://pbs.twimg.com/media/FglVYVmXkAIWB5w.jpg",
"type": "photo"
}
],
"url": "https://twitter.com/elonmusk/status/1587894226695884800",
"created_at": "2022-11-02T19:47:39.000Z",
"quote_count": 63550,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1236029449042198528",
"conversation_id": "1236029449042198528",
"full_text": "The coronavirus panic is dumb",
"reply_count": 39483,
"retweet_count": 267574,
"favorite_count": 1453833,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [],
"url": "https://twitter.com/elonmusk/status/1236029449042198528",
"created_at": "2020-03-06T20:42:39.000Z",
"quote_count": 38185,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1519020176884305920",
"conversation_id": "1519020176884305920",
"full_text": "The extreme antibody reaction from those who fear free speech says it all",
"reply_count": 76483,
"retweet_count": 182948,
"favorite_count": 1586156,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [],
"url": "https://twitter.com/elonmusk/status/1519020176884305920",
"created_at": "2022-04-26T18:26:46.000Z",
"quote_count": 17843,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1685096284275802112",
"conversation_id": "1685096284275802112",
"full_text": "https://t.co/XEydRiST9D",
"reply_count": 129650,
"retweet_count": 100921,
"favorite_count": 1672309,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [
{
"media_url": "https://pbs.twimg.com/media/F2KqI_ZXUAAGRCD.jpg",
"type": "photo"
}
],
"url": "https://twitter.com/elonmusk/status/1685096284275802112",
"created_at": "2023-07-29T01:13:56.000Z",
"view_count": 153952239,
"quote_count": 65149,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1519735033950470144",
"conversation_id": "1519735033950470144",
"full_text": "https://t.co/Q9OjlJhi7f",
"reply_count": 88482,
"retweet_count": 199755,
"favorite_count": 1498481,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [
{
"media_url": "https://pbs.twimg.com/media/FRcu9TeXEAMjvTM.jpg",
"type": "photo"
}
],
"url": "https://twitter.com/elonmusk/status/1519735033950470144",
"created_at": "2022-04-28T17:47:22.000Z",
"quote_count": 43234,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1519415674111672325",
"conversation_id": "1519415674111672325",
"full_text": "For Twitter to deserve public trust, it must be politically neutral, which effectively means upsetting the far right and the far left equally",
"reply_count": 77096,
"retweet_count": 137275,
"favorite_count": 1489409,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [],
"url": "https://twitter.com/elonmusk/status/1519415674111672325",
"created_at": "2022-04-27T20:38:20.000Z",
"quote_count": 28306,
"is_quote_tweet": false,
"is_retweet": false,
"is_pinned": false,
"is_truncated": false,
"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1479236333516165121",
"conversation_id": "1479236333516165121",
"full_text": "Starlinks with “lasers” deployed to orbit https://t.co/Y1eg9gl7sJ",
"reply_count": 12544,
"retweet_count": 9849,
"favorite_count": 915843,
"hashtags": [],
"symbols": [],
"user_mentions": [],
"urls": [],
"media": [
{
"media_url": "https://pbs.twimg.com/media/FIdNmXtVkAEF0Ss.jpg",
"type": "photo"
}
],
"url": "https://twitter.com/elonmusk/status/1479236333516165121",
"created_at": "2022-01-06T23:39:59.000Z",
"quote_count": 1185,
"is_quote_tweet": false,
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}
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"startUrl": "https://twitter.com/elonmusk/with_replies"
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"type": "photo"
}
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"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
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"id": "1658960642445910017",
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}
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"url": "https://twitter.com/elonmusk/status/1658960642445910017",
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"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1358319935978496001",
"conversation_id": "1358319935978496001",
"full_text": "So … it’s finally come to this … https://t.co/Gf0Rg2QOaF",
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"url": "https://twitter.com/elonmusk/status/1358319935978496001",
"created_at": "2021-02-07T07:41:23.000Z",
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"startUrl": "https://twitter.com/elonmusk/with_replies"
},
{
"username": "elonmusk",
"user_id": "44196397",
"id": "1129274835173908481",
"conversation_id": "1129274835173908481",
"full_text": "And I am forever grateful https://t.co/kU1pT8t0yv",
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}
],
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"created_at": "2019-05-17T06:37:56.000Z",
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},
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}] | 0 |
public_repos | public_repos/twitter-finetune/ingest.py | import json
from langchain.schema import AIMessage
from langchain.adapters.openai import convert_message_to_dict
import time
import openai
from io import BytesIO
if __name__ == "__main__":
with open('dataset_twitter-scraper_2023-08-23_22-13-19-740.json') as f:
data = json.load(f)
tweets = [d["full_text"] for d in data if "t.co" not in d['full_text']]
messages = [AIMessage(content=t) for t in tweets]
system_message = {"role": "system", "content": "write a tweet"}
data = [[system_message, convert_message_to_dict(m)] for m in messages]
my_file = BytesIO()
for m in data:
my_file.write((json.dumps({"messages": m}) + "\n").encode('utf-8'))
my_file.seek(0)
training_file = openai.File.create(
file=my_file,
purpose='fine-tune'
)
while True:
try:
job = openai.FineTuningJob.create(training_file=training_file.id, model="gpt-3.5-turbo")
except Exception as e:
print(e)
print("Trying again in ten seconds....")
time.sleep(10)
start = time.time()
while True:
ftj = openai.FineTuningJob.retrieve(job.id)
if ftj.fine_tuned_model is None:
print(f"Waiting for fine-tuning to complete... Elapsed: {time.time() - start}", end="\r", flush=True)
time.sleep(10)
else:
print("\n")
print(ftj.fine_tuned_model, flush=True)
break
| 0 |
public_repos | public_repos/numpy-stubs/.flake8 | # Some PEP8 deviations are considered irrelevant to stub files:
# (error counts as of 2017-05-22)
# 17952 E704 multiple statements on one line (def)
# 12197 E301 expected 1 blank line
# 7155 E302 expected 2 blank lines
# 1463 F401 imported but unused
# 967 E701 multiple statements on one line (colon)
# 457 F811 redefinition
# 390 E305 expected 2 blank lines
# 4 E741 ambiguous variable name
# Nice-to-haves ignored for now
# 2307 E501 line too long
# Other ignored warnings
# W504 line break after binary operator
[flake8]
ignore = F401, F403, F405, F811, E301, E302, E305, E501, E701, E704, E741, B303, W504 | 0 |
public_repos | public_repos/numpy-stubs/setup.cfg | # Ignore the following errors (for stubs):
# E301 expected 1 blank line, found 0
# E302 expected 2 blank lines, found 1
# E305 expected 2 blank lines after class or function definition, found 1
# E701 multiple statements on one line (colon)
# E704 multiple statements on one line (def)
[flake8]
ignore = E301, E302, E305, E701, E704
exclude = .git,__pycache__,tests/reveal
| 0 |
public_repos | public_repos/numpy-stubs/setup.py | from setuptools import setup
import os
def find_stubs(package):
stubs = []
for root, dirs, files in os.walk(package):
for file in files:
path = os.path.join(root, file).replace(package + os.sep, "", 1)
stubs.append(path)
return {package: stubs}
setup(
name="numpy-stubs",
maintainer="NumPy Developers",
maintainer_email="numpy-discussion@python.org",
description="PEP 561 type stubs for numpy",
url="http://www.numpy.org",
license="BSD",
version="0.0.1",
packages=["numpy-stubs"],
# PEP 561 requires these
install_requires=[
"numpy>=1.16.0",
'typing_extensions>=3.7.4; python_version<"3.8"',
],
package_data=find_stubs("numpy-stubs"),
zip_safe=False,
)
| 0 |
public_repos | public_repos/numpy-stubs/.travis.yml | language: python
matrix:
include:
- python: 3.8
dist: xenial
- python: 3.7
dist: xenial
- python: 3.6
dist: xenial
notifications:
email: false
install:
- pip install -r test-requirements.txt
- pip install .
script:
- flake8
- black --check .
- py.test
cache:
directories:
- "$HOME/.cache/pip"
| 0 |
public_repos | public_repos/numpy-stubs/runtests.py | #!/usr/bin/env python
import argparse
import ast
import importlib
import os
import subprocess
import sys
import pytest
STUBS_ROOT = os.path.dirname(os.path.abspath(__file__))
# Technically "public" functions (they don't start with an underscore)
# that we don't want to include.
BLACKLIST = {
"numpy": {
# Stdlib modules in the namespace by accident
"absolute_import",
"division",
"print_function",
"warnings",
# Accidentally public, deprecated, or shouldn't be used
"Tester",
"add_docstring",
"add_newdoc",
"add_newdoc_ufunc",
"core",
"fastCopyAndTranspose",
"get_array_wrap",
"int_asbuffer",
"oldnumeric",
"safe_eval",
"set_numeric_ops",
"test",
# Builtins
"bool",
"complex",
"float",
"int",
"long",
"object",
"str",
"unicode",
# Should use numpy_financial instead
"fv",
"ipmt",
"irr",
"mirr",
"nper",
"npv",
"pmt",
"ppmt",
"pv",
"rate",
# More standard names should be preferred
"alltrue", # all
"sometrue", # any
}
}
class FindAttributes(ast.NodeVisitor):
"""Find top-level attributes/functions/classes in the stubs.
Do this by walking the stubs ast. See e.g.
https://greentreesnakes.readthedocs.io/en/latest/index.html
for more information on working with Python's ast.
"""
def __init__(self):
self.attributes = set()
def visit_FunctionDef(self, node):
if node.name == "__getattr__":
# Not really a module member.
return
self.attributes.add(node.name)
# Do not call self.generic_visit; we are only interested in
# top-level functions.
return
def visit_ClassDef(self, node):
if not node.name.startswith("_"):
self.attributes.add(node.name)
return
def visit_AnnAssign(self, node):
self.attributes.add(node.target.id)
def find_missing(module_name):
module_path = os.path.join(
STUBS_ROOT,
module_name.replace("numpy", "numpy-stubs").replace(".", os.sep),
"__init__.pyi",
)
module = importlib.import_module(module_name)
module_attributes = {
attribute for attribute in dir(module) if not attribute.startswith("_")
}
if os.path.isfile(module_path):
with open(module_path) as f:
tree = ast.parse(f.read())
ast_visitor = FindAttributes()
ast_visitor.visit(tree)
stubs_attributes = ast_visitor.attributes
else:
# No stubs for this module yet.
stubs_attributes = set()
blacklist = BLACKLIST.get(module_name, set())
missing = module_attributes - stubs_attributes - blacklist
print("\n".join(sorted(missing)))
def run_pytest(argv):
subprocess.run(
[sys.executable, "-m", "pip", "install", STUBS_ROOT],
capture_output=True,
check=True,
)
return pytest.main([STUBS_ROOT] + argv)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--find-missing")
args, remaining_argv = parser.parse_known_args()
if args.find_missing is not None:
find_missing(args.find_missing)
sys.exit(0)
sys.exit(run_pytest(remaining_argv))
if __name__ == "__main__":
main()
| 0 |
public_repos | public_repos/numpy-stubs/test-requirements.txt | black==18.9b0
flake8==3.7.9
flake8-pyi==18.3.1
pytest==4.0.0
mypy==0.770
| 0 |
public_repos | public_repos/numpy-stubs/README.md | **These stubs have been merged into NumPy, and all further development will happen in the NumPy main repo. We welcome your contributions there!**
| 0 |
public_repos | public_repos/numpy-stubs/LICENSE | Copyright (c) 2005-2017, NumPy Developers.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are
met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of the NumPy Developers nor the names of any
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
| 0 |
public_repos/numpy-stubs | public_repos/numpy-stubs/numpy-stubs/__init__.pyi | import builtins
import sys
import datetime as dt
from abc import abstractmethod
from numpy.core._internal import _ctypes
from numpy.typing import ArrayLike, DtypeLike, _Shape, _ShapeLike
from typing import (
Any,
ByteString,
Callable,
Container,
Callable,
Dict,
Generic,
IO,
Iterable,
List,
Mapping,
Optional,
overload,
Sequence,
Sized,
SupportsAbs,
SupportsComplex,
SupportsFloat,
SupportsInt,
Text,
Tuple,
Type,
TypeVar,
Union,
)
if sys.version_info[0] < 3:
class SupportsBytes: ...
else:
from typing import SupportsBytes
if sys.version_info >= (3, 8):
from typing import Literal, Protocol
else:
from typing_extensions import Literal, Protocol
# TODO: remove when the full numpy namespace is defined
def __getattr__(name: str) -> Any: ...
_NdArraySubClass = TypeVar("_NdArraySubClass", bound=ndarray)
class dtype:
names: Optional[Tuple[str, ...]]
def __init__(self, obj: DtypeLike, align: bool = ..., copy: bool = ...) -> None: ...
def __eq__(self, other: DtypeLike) -> bool: ...
def __ne__(self, other: DtypeLike) -> bool: ...
def __gt__(self, other: DtypeLike) -> bool: ...
def __ge__(self, other: DtypeLike) -> bool: ...
def __lt__(self, other: DtypeLike) -> bool: ...
def __le__(self, other: DtypeLike) -> bool: ...
@property
def alignment(self) -> int: ...
@property
def base(self) -> dtype: ...
@property
def byteorder(self) -> str: ...
@property
def char(self) -> str: ...
@property
def descr(self) -> List[Union[Tuple[str, str], Tuple[str, str, _Shape]]]: ...
@property
def fields(
self,
) -> Optional[Mapping[str, Union[Tuple[dtype, int], Tuple[dtype, int, Any]]]]: ...
@property
def flags(self) -> int: ...
@property
def hasobject(self) -> bool: ...
@property
def isbuiltin(self) -> int: ...
@property
def isnative(self) -> bool: ...
@property
def isalignedstruct(self) -> bool: ...
@property
def itemsize(self) -> int: ...
@property
def kind(self) -> str: ...
@property
def metadata(self) -> Optional[Mapping[str, Any]]: ...
@property
def name(self) -> str: ...
@property
def num(self) -> int: ...
@property
def shape(self) -> _Shape: ...
@property
def ndim(self) -> int: ...
@property
def subdtype(self) -> Optional[Tuple[dtype, _Shape]]: ...
def newbyteorder(self, new_order: str = ...) -> dtype: ...
# Leave str and type for end to avoid having to use `builtins.str`
# everywhere. See https://github.com/python/mypy/issues/3775
@property
def str(self) -> builtins.str: ...
@property
def type(self) -> Type[generic]: ...
_Dtype = dtype # to avoid name conflicts with ndarray.dtype
class _flagsobj:
aligned: bool
updateifcopy: bool
writeable: bool
writebackifcopy: bool
@property
def behaved(self) -> bool: ...
@property
def c_contiguous(self) -> bool: ...
@property
def carray(self) -> bool: ...
@property
def contiguous(self) -> bool: ...
@property
def f_contiguous(self) -> bool: ...
@property
def farray(self) -> bool: ...
@property
def fnc(self) -> bool: ...
@property
def forc(self) -> bool: ...
@property
def fortran(self) -> bool: ...
@property
def num(self) -> int: ...
@property
def owndata(self) -> bool: ...
def __getitem__(self, key: str) -> bool: ...
def __setitem__(self, key: str, value: bool) -> None: ...
_FlatIterSelf = TypeVar("_FlatIterSelf", bound=flatiter)
class flatiter(Generic[_ArraySelf]):
@property
def base(self) -> _ArraySelf: ...
@property
def coords(self) -> _Shape: ...
@property
def index(self) -> int: ...
def copy(self) -> _ArraySelf: ...
def __iter__(self: _FlatIterSelf) -> _FlatIterSelf: ...
def __next__(self) -> generic: ...
_ArraySelf = TypeVar("_ArraySelf", bound=_ArrayOrScalarCommon)
class _ArrayOrScalarCommon(
SupportsInt, SupportsFloat, SupportsComplex, SupportsBytes, SupportsAbs[Any]
):
@property
def T(self: _ArraySelf) -> _ArraySelf: ...
@property
def base(self) -> Optional[ndarray]: ...
@property
def dtype(self) -> _Dtype: ...
@property
def data(self) -> memoryview: ...
@property
def flags(self) -> _flagsobj: ...
@property
def size(self) -> int: ...
@property
def itemsize(self) -> int: ...
@property
def nbytes(self) -> int: ...
@property
def ndim(self) -> int: ...
@property
def shape(self) -> _Shape: ...
@property
def strides(self) -> _Shape: ...
def __array__(self, __dtype: DtypeLike = ...) -> ndarray: ...
def __int__(self) -> int: ...
def __float__(self) -> float: ...
def __complex__(self) -> complex: ...
if sys.version_info[0] < 3:
def __oct__(self) -> str: ...
def __hex__(self) -> str: ...
def __nonzero__(self) -> bool: ...
def __unicode__(self) -> Text: ...
else:
def __bool__(self) -> bool: ...
def __bytes__(self) -> bytes: ...
def __str__(self) -> str: ...
def __repr__(self) -> str: ...
def __copy__(self: _ArraySelf, order: str = ...) -> _ArraySelf: ...
def __deepcopy__(self: _ArraySelf, memo: dict) -> _ArraySelf: ...
def __lt__(self, other): ...
def __le__(self, other): ...
def __eq__(self, other): ...
def __ne__(self, other): ...
def __gt__(self, other): ...
def __ge__(self, other): ...
def __add__(self, other): ...
def __radd__(self, other): ...
def __iadd__(self, other): ...
def __sub__(self, other): ...
def __rsub__(self, other): ...
def __isub__(self, other): ...
def __mul__(self, other): ...
def __rmul__(self, other): ...
def __imul__(self, other): ...
if sys.version_info[0] < 3:
def __div__(self, other): ...
def __rdiv__(self, other): ...
def __idiv__(self, other): ...
def __truediv__(self, other): ...
def __rtruediv__(self, other): ...
def __itruediv__(self, other): ...
def __floordiv__(self, other): ...
def __rfloordiv__(self, other): ...
def __ifloordiv__(self, other): ...
def __mod__(self, other): ...
def __rmod__(self, other): ...
def __imod__(self, other): ...
def __divmod__(self, other): ...
def __rdivmod__(self, other): ...
# NumPy's __pow__ doesn't handle a third argument
def __pow__(self, other): ...
def __rpow__(self, other): ...
def __ipow__(self, other): ...
def __lshift__(self, other): ...
def __rlshift__(self, other): ...
def __ilshift__(self, other): ...
def __rshift__(self, other): ...
def __rrshift__(self, other): ...
def __irshift__(self, other): ...
def __and__(self, other): ...
def __rand__(self, other): ...
def __iand__(self, other): ...
def __xor__(self, other): ...
def __rxor__(self, other): ...
def __ixor__(self, other): ...
def __or__(self, other): ...
def __ror__(self, other): ...
def __ior__(self, other): ...
if sys.version_info[:2] >= (3, 5):
def __matmul__(self, other): ...
def __rmatmul__(self, other): ...
def __neg__(self: _ArraySelf) -> _ArraySelf: ...
def __pos__(self: _ArraySelf) -> _ArraySelf: ...
def __abs__(self: _ArraySelf) -> _ArraySelf: ...
def __invert__(self: _ArraySelf) -> _ArraySelf: ...
# TODO(shoyer): remove when all methods are defined
def __getattr__(self, name) -> Any: ...
_BufferType = Union[ndarray, bytes, bytearray, memoryview]
class ndarray(_ArrayOrScalarCommon, Iterable, Sized, Container):
@property
def real(self: _ArraySelf) -> _ArraySelf: ...
@real.setter
def real(self, value: ArrayLike) -> None: ...
@property
def imag(self: _ArraySelf) -> _ArraySelf: ...
@imag.setter
def imag(self, value: ArrayLike) -> None: ...
def __new__(
cls: Type[_ArraySelf],
shape: Sequence[int],
dtype: DtypeLike = ...,
buffer: _BufferType = ...,
offset: int = ...,
strides: _ShapeLike = ...,
order: Optional[str] = ...,
) -> _ArraySelf: ...
@property
def dtype(self) -> _Dtype: ...
@property
def ctypes(self) -> _ctypes: ...
@property
def shape(self) -> _Shape: ...
@shape.setter
def shape(self, value: _ShapeLike): ...
@property
def flat(self: _ArraySelf) -> flatiter[_ArraySelf]: ...
@property
def strides(self) -> _Shape: ...
@strides.setter
def strides(self, value: _ShapeLike): ...
# Array conversion
@overload
def item(self, *args: int) -> Any: ...
@overload
def item(self, args: Tuple[int, ...]) -> Any: ...
def tolist(self) -> List[Any]: ...
@overload
def itemset(self, __value: Any) -> None: ...
@overload
def itemset(self, __item: _ShapeLike, __value: Any) -> None: ...
def tostring(self, order: Optional[str] = ...) -> bytes: ...
def tobytes(self, order: Optional[str] = ...) -> bytes: ...
def tofile(
self, fid: Union[IO[bytes], str], sep: str = ..., format: str = ...
) -> None: ...
def dump(self, file: str) -> None: ...
def dumps(self) -> bytes: ...
def astype(
self: _ArraySelf,
dtype: DtypeLike,
order: str = ...,
casting: str = ...,
subok: bool = ...,
copy: bool = ...,
) -> _ArraySelf: ...
def byteswap(self: _ArraySelf, inplace: bool = ...) -> _ArraySelf: ...
def copy(self: _ArraySelf, order: str = ...) -> _ArraySelf: ...
@overload
def view(self, type: Type[_NdArraySubClass]) -> _NdArraySubClass: ...
@overload
def view(self: _ArraySelf, dtype: DtypeLike = ...) -> _ArraySelf: ...
@overload
def view(
self, dtype: DtypeLike, type: Type[_NdArraySubClass]
) -> _NdArraySubClass: ...
def getfield(
self: _ArraySelf, dtype: DtypeLike, offset: int = ...
) -> _ArraySelf: ...
def setflags(
self, write: bool = ..., align: bool = ..., uic: bool = ...
) -> None: ...
def fill(self, value: Any) -> None: ...
# Shape manipulation
@overload
def reshape(
self: _ArraySelf, shape: Sequence[int], *, order: str = ...
) -> _ArraySelf: ...
@overload
def reshape(self: _ArraySelf, *shape: int, order: str = ...) -> _ArraySelf: ...
@overload
def resize(self, new_shape: Sequence[int], *, refcheck: bool = ...) -> None: ...
@overload
def resize(self, *new_shape: int, refcheck: bool = ...) -> None: ...
@overload
def transpose(self: _ArraySelf, axes: Sequence[int]) -> _ArraySelf: ...
@overload
def transpose(self: _ArraySelf, *axes: int) -> _ArraySelf: ...
def swapaxes(self: _ArraySelf, axis1: int, axis2: int) -> _ArraySelf: ...
def flatten(self: _ArraySelf, order: str = ...) -> _ArraySelf: ...
def ravel(self: _ArraySelf, order: str = ...) -> _ArraySelf: ...
def squeeze(
self: _ArraySelf, axis: Union[int, Tuple[int, ...]] = ...
) -> _ArraySelf: ...
# Many of these special methods are irrelevant currently, since protocols
# aren't supported yet. That said, I'm adding them for completeness.
# https://docs.python.org/3/reference/datamodel.html
def __len__(self) -> int: ...
def __getitem__(self, key) -> Any: ...
def __setitem__(self, key, value): ...
def __iter__(self) -> Any: ...
def __contains__(self, key) -> bool: ...
def __index__(self) -> int: ...
# NOTE: while `np.generic` is not technically an instance of `ABCMeta`,
# the `@abstractmethod` decorator is herein used to (forcefully) deny
# the creation of `np.generic` instances.
# The `# type: ignore` comments are necessary to silence mypy errors regarding
# the missing `ABCMeta` metaclass.
# See https://github.com/numpy/numpy-stubs/pull/80 for more details.
class generic(_ArrayOrScalarCommon):
@abstractmethod
def __init__(self, *args: Any, **kwargs: Any) -> None: ...
@property
def base(self) -> None: ...
class _real_generic(generic): # type: ignore
@property
def real(self: _ArraySelf) -> _ArraySelf: ...
@property
def imag(self: _ArraySelf) -> _ArraySelf: ...
class number(generic): ... # type: ignore
class bool_(_real_generic):
def __init__(self, value: object = ...) -> None: ...
class object_(generic):
def __init__(self, value: object = ...) -> None: ...
class datetime64:
@overload
def __init__(
self, _data: Union[datetime64, str, dt.datetime] = ..., _format: str = ...
) -> None: ...
@overload
def __init__(self, _data: int, _format: str) -> None: ...
def __add__(self, other: Union[timedelta64, int]) -> datetime64: ...
def __sub__(self, other: Union[timedelta64, datetime64, int]) -> timedelta64: ...
class integer(number, _real_generic): ... # type: ignore
class signedinteger(integer): ... # type: ignore
class int8(signedinteger):
def __init__(self, value: SupportsInt = ...) -> None: ...
class int16(signedinteger):
def __init__(self, value: SupportsInt = ...) -> None: ...
class int32(signedinteger):
def __init__(self, value: SupportsInt = ...) -> None: ...
class int64(signedinteger):
def __init__(self, value: SupportsInt = ...) -> None: ...
class timedelta64(signedinteger):
def __init__(self, _data: Any = ..., _format: str = ...) -> None: ...
@overload
def __add__(self, other: Union[timedelta64, int]) -> timedelta64: ...
@overload
def __add__(self, other: datetime64) -> datetime64: ...
def __sub__(self, other: Union[timedelta64, int]) -> timedelta64: ...
if sys.version_info[0] < 3:
@overload
def __div__(self, other: timedelta64) -> float: ...
@overload
def __div__(self, other: float) -> timedelta64: ...
@overload
def __truediv__(self, other: timedelta64) -> float: ...
@overload
def __truediv__(self, other: float) -> timedelta64: ...
def __mod__(self, other: timedelta64) -> timedelta64: ...
class unsignedinteger(integer): ... # type: ignore
class uint8(unsignedinteger):
def __init__(self, value: SupportsInt = ...) -> None: ...
class uint16(unsignedinteger):
def __init__(self, value: SupportsInt = ...) -> None: ...
class uint32(unsignedinteger):
def __init__(self, value: SupportsInt = ...) -> None: ...
class uint64(unsignedinteger):
def __init__(self, value: SupportsInt = ...) -> None: ...
class inexact(number): ... # type: ignore
class floating(inexact, _real_generic): ... # type: ignore
class float16(floating):
def __init__(self, value: SupportsFloat = ...) -> None: ...
class float32(floating):
def __init__(self, value: SupportsFloat = ...) -> None: ...
class float64(floating):
def __init__(self, value: SupportsFloat = ...) -> None: ...
class complexfloating(inexact): ... # type: ignore
class complex64(complexfloating):
def __init__(
self, value: Union[SupportsInt, SupportsFloat, SupportsComplex] = ...
) -> None: ...
@property
def real(self) -> float32: ...
@property
def imag(self) -> float32: ...
class complex128(complexfloating):
def __init__(
self, value: Union[SupportsInt, SupportsFloat, SupportsComplex] = ...
) -> None: ...
@property
def real(self) -> float64: ...
@property
def imag(self) -> float64: ...
class flexible(_real_generic): ... # type: ignore
class void(flexible):
def __init__(self, value: Union[int, integer, bool_, bytes, bytes_]): ...
class character(_real_generic): ... # type: ignore
class bytes_(character):
@overload
def __init__(self, value: object = ...) -> None: ...
@overload
def __init__(
self, value: object, encoding: str = ..., errors: str = ...
) -> None: ...
class str_(character):
@overload
def __init__(self, value: object = ...) -> None: ...
@overload
def __init__(
self, value: object, encoding: str = ..., errors: str = ...
) -> None: ...
# TODO(alan): Platform dependent types
# longcomplex, longdouble, longfloat
# bytes, short, intc, intp, longlong
# half, single, double, longdouble
# uint_, int_, float_, complex_
# float128, complex256
# float96
def array(
object: object,
dtype: DtypeLike = ...,
copy: bool = ...,
subok: bool = ...,
ndmin: int = ...,
) -> ndarray: ...
def zeros(
shape: _ShapeLike, dtype: DtypeLike = ..., order: Optional[str] = ...
) -> ndarray: ...
def ones(
shape: _ShapeLike, dtype: DtypeLike = ..., order: Optional[str] = ...
) -> ndarray: ...
def empty(
shape: _ShapeLike, dtype: DtypeLike = ..., order: Optional[str] = ...
) -> ndarray: ...
def zeros_like(
a: ArrayLike,
dtype: DtypeLike = ...,
order: str = ...,
subok: bool = ...,
shape: Optional[Union[int, Sequence[int]]] = ...,
) -> ndarray: ...
def ones_like(
a: ArrayLike,
dtype: DtypeLike = ...,
order: str = ...,
subok: bool = ...,
shape: Optional[_ShapeLike] = ...,
) -> ndarray: ...
def empty_like(
a: ArrayLike,
dtype: DtypeLike = ...,
order: str = ...,
subok: bool = ...,
shape: Optional[_ShapeLike] = ...,
) -> ndarray: ...
def full(
shape: _ShapeLike, fill_value: Any, dtype: DtypeLike = ..., order: str = ...
) -> ndarray: ...
def full_like(
a: ArrayLike,
fill_value: Any,
dtype: DtypeLike = ...,
order: str = ...,
subok: bool = ...,
shape: Optional[_ShapeLike] = ...,
) -> ndarray: ...
def count_nonzero(
a: ArrayLike, axis: Optional[Union[int, Tuple[int], Tuple[int, int]]] = ...
) -> Union[int, ndarray]: ...
def isfortran(a: ndarray) -> bool: ...
def argwhere(a: ArrayLike) -> ndarray: ...
def flatnonzero(a: ArrayLike) -> ndarray: ...
def correlate(a: ArrayLike, v: ArrayLike, mode: str = ...) -> ndarray: ...
def convolve(a: ArrayLike, v: ArrayLike, mode: str = ...) -> ndarray: ...
def outer(a: ArrayLike, b: ArrayLike, out: ndarray = ...) -> ndarray: ...
def tensordot(
a: ArrayLike,
b: ArrayLike,
axes: Union[
int, Tuple[int, int], Tuple[Tuple[int, int], ...], Tuple[List[int, int], ...]
] = ...,
) -> ndarray: ...
def roll(
a: ArrayLike,
shift: Union[int, Tuple[int, ...]],
axis: Optional[Union[int, Tuple[int, ...]]] = ...,
) -> ndarray: ...
def rollaxis(a: ArrayLike, axis: int, start: int = ...) -> ndarray: ...
def moveaxis(
a: ndarray,
source: Union[int, Sequence[int]],
destination: Union[int, Sequence[int]],
) -> ndarray: ...
def cross(
a: ArrayLike,
b: ArrayLike,
axisa: int = ...,
axisb: int = ...,
axisc: int = ...,
axis: Optional[int] = ...,
) -> ndarray: ...
def indices(
dimensions: Sequence[int], dtype: dtype = ..., sparse: bool = ...
) -> Union[ndarray, Tuple[ndarray, ...]]: ...
def fromfunction(function: Callable, shape: Tuple[int, int], **kwargs) -> Any: ...
def isscalar(element: Any) -> bool: ...
def binary_repr(num: int, width: Optional[int] = ...) -> str: ...
def base_repr(number: int, base: int = ..., padding: int = ...) -> str: ...
def identity(n: int, dtype: DtypeLike = ...) -> ndarray: ...
def allclose(
a: ArrayLike,
b: ArrayLike,
rtol: float = ...,
atol: float = ...,
equal_nan: bool = ...,
) -> bool: ...
def isclose(
a: ArrayLike,
b: ArrayLike,
rtol: float = ...,
atol: float = ...,
equal_nan: bool = ...,
) -> Union[bool_, ndarray]: ...
def array_equal(a1: ArrayLike, a2: ArrayLike) -> bool: ...
def array_equiv(a1: ArrayLike, a2: ArrayLike) -> bool: ...
#
# Constants
#
Inf: float
Infinity: float
NAN: float
NINF: float
NZERO: float
NaN: float
PINF: float
PZERO: float
e: float
euler_gamma: float
inf: float
infty: float
nan: float
pi: float
ALLOW_THREADS: int
BUFSIZE: int
CLIP: int
ERR_CALL: int
ERR_DEFAULT: int
ERR_IGNORE: int
ERR_LOG: int
ERR_PRINT: int
ERR_RAISE: int
ERR_WARN: int
FLOATING_POINT_SUPPORT: int
FPE_DIVIDEBYZERO: int
FPE_INVALID: int
FPE_OVERFLOW: int
FPE_UNDERFLOW: int
MAXDIMS: int
MAY_SHARE_BOUNDS: int
MAY_SHARE_EXACT: int
RAISE: int
SHIFT_DIVIDEBYZERO: int
SHIFT_INVALID: int
SHIFT_OVERFLOW: int
SHIFT_UNDERFLOW: int
UFUNC_BUFSIZE_DEFAULT: int
WRAP: int
little_endian: int
tracemalloc_domain: int
class ufunc:
@property
def __name__(self) -> str: ...
def __call__(
self,
*args: ArrayLike,
out: Optional[Union[ndarray, Tuple[ndarray, ...]]] = ...,
where: Optional[ndarray] = ...,
# The list should be a list of tuples of ints, but since we
# don't know the signature it would need to be
# Tuple[int, ...]. But, since List is invariant something like
# e.g. List[Tuple[int, int]] isn't a subtype of
# List[Tuple[int, ...]], so we can't type precisely here.
axes: List[Any] = ...,
axis: int = ...,
keepdims: bool = ...,
# TODO: make this precise when we can use Literal.
casting: str = ...,
# TODO: make this precise when we can use Literal.
order: Optional[str] = ...,
dtype: DtypeLike = ...,
subok: bool = ...,
signature: Union[str, Tuple[str]] = ...,
# In reality this should be a length of list 3 containing an
# int, an int, and a callable, but there's no way to express
# that.
extobj: List[Union[int, Callable]] = ...,
) -> Union[ndarray, generic]: ...
@property
def nin(self) -> int: ...
@property
def nout(self) -> int: ...
@property
def nargs(self) -> int: ...
@property
def ntypes(self) -> int: ...
@property
def types(self) -> List[str]: ...
# Broad return type because it has to encompass things like
#
# >>> np.logical_and.identity is True
# True
# >>> np.add.identity is 0
# True
# >>> np.sin.identity is None
# True
#
# and any user-defined ufuncs.
@property
def identity(self) -> Any: ...
# This is None for ufuncs and a string for gufuncs.
@property
def signature(self) -> Optional[str]: ...
# The next four methods will always exist, but they will just
# raise a ValueError ufuncs with that don't accept two input
# arguments and return one output argument. Because of that we
# can't type them very precisely.
@property
def reduce(self) -> Any: ...
@property
def accumulate(self) -> Any: ...
@property
def reduceat(self) -> Any: ...
@property
def outer(self) -> Any: ...
# Similarly at won't be defined for ufuncs that return multiple
# outputs, so we can't type it very precisely.
@property
def at(self) -> Any: ...
absolute: ufunc
add: ufunc
arccos: ufunc
arccosh: ufunc
arcsin: ufunc
arcsinh: ufunc
arctan2: ufunc
arctan: ufunc
arctanh: ufunc
bitwise_and: ufunc
bitwise_or: ufunc
bitwise_xor: ufunc
cbrt: ufunc
ceil: ufunc
conjugate: ufunc
copysign: ufunc
cos: ufunc
cosh: ufunc
deg2rad: ufunc
degrees: ufunc
divmod: ufunc
equal: ufunc
exp2: ufunc
exp: ufunc
expm1: ufunc
fabs: ufunc
float_power: ufunc
floor: ufunc
floor_divide: ufunc
fmax: ufunc
fmin: ufunc
fmod: ufunc
frexp: ufunc
gcd: ufunc
greater: ufunc
greater_equal: ufunc
heaviside: ufunc
hypot: ufunc
invert: ufunc
isfinite: ufunc
isinf: ufunc
isnan: ufunc
isnat: ufunc
lcm: ufunc
ldexp: ufunc
left_shift: ufunc
less: ufunc
less_equal: ufunc
log10: ufunc
log1p: ufunc
log2: ufunc
log: ufunc
logaddexp2: ufunc
logaddexp: ufunc
logical_and: ufunc
logical_not: ufunc
logical_or: ufunc
logical_xor: ufunc
matmul: ufunc
maximum: ufunc
minimum: ufunc
modf: ufunc
multiply: ufunc
negative: ufunc
nextafter: ufunc
not_equal: ufunc
positive: ufunc
power: ufunc
rad2deg: ufunc
radians: ufunc
reciprocal: ufunc
remainder: ufunc
right_shift: ufunc
rint: ufunc
sign: ufunc
signbit: ufunc
sin: ufunc
sinh: ufunc
spacing: ufunc
sqrt: ufunc
square: ufunc
subtract: ufunc
tan: ufunc
tanh: ufunc
true_divide: ufunc
trunc: ufunc
# Warnings
class ModuleDeprecationWarning(DeprecationWarning): ...
class VisibleDeprecationWarning(UserWarning): ...
class ComplexWarning(RuntimeWarning): ...
class RankWarning(UserWarning): ...
# Errors
class TooHardError(RuntimeError): ...
class AxisError(ValueError, IndexError):
def __init__(
self, axis: int, ndim: Optional[int] = ..., msg_prefix: Optional[str] = ...
) -> None: ...
# Functions from np.core.numerictypes
_DefaultType = TypeVar("_DefaultType")
def maximum_sctype(t: DtypeLike) -> dtype: ...
def issctype(rep: object) -> bool: ...
@overload
def obj2sctype(rep: object) -> Optional[generic]: ...
@overload
def obj2sctype(rep: object, default: None) -> Optional[generic]: ...
@overload
def obj2sctype(
rep: object, default: Type[_DefaultType]
) -> Union[generic, Type[_DefaultType]]: ...
def issubclass_(arg1: object, arg2: Union[object, Tuple[object, ...]]) -> bool: ...
def issubsctype(
arg1: Union[ndarray, DtypeLike], arg2: Union[ndarray, DtypeLike]
) -> bool: ...
def issubdtype(arg1: DtypeLike, arg2: DtypeLike) -> bool: ...
def sctype2char(sctype: object) -> str: ...
def find_common_type(
array_types: Sequence[DtypeLike], scalar_types: Sequence[DtypeLike]
) -> dtype: ...
# Functions from np.core.fromnumeric
_Mode = Literal["raise", "wrap", "clip"]
_Order = Literal["C", "F", "A"]
_PartitionKind = Literal["introselect"]
_SortKind = Literal["quicksort", "mergesort", "heapsort", "stable"]
_Side = Literal["left", "right"]
# Various annotations for scalars
# While dt.datetime and dt.timedelta are not technically part of NumPy,
# they are one of the rare few builtin scalars which serve as valid return types.
# See https://github.com/numpy/numpy-stubs/pull/67#discussion_r412604113.
_ScalarNumpy = Union[generic, dt.datetime, dt.timedelta]
_ScalarBuiltin = Union[str, bytes, dt.date, dt.timedelta, bool, int, float, complex]
_Scalar = Union[_ScalarBuiltin, _ScalarNumpy]
# Integers and booleans can generally be used interchangeably
_ScalarIntOrBool = TypeVar("_ScalarIntOrBool", bound=Union[integer, bool_])
_ScalarGeneric = TypeVar("_ScalarGeneric", bound=generic)
_ScalarGenericDT = TypeVar(
"_ScalarGenericDT", bound=Union[dt.datetime, dt.timedelta, generic]
)
# An array-like object consisting of integers
_Int = Union[int, integer]
_Bool = Union[bool, bool_]
_IntOrBool = Union[_Int, _Bool]
_ArrayLikeIntNested = ArrayLike # TODO: wait for support for recursive types
_ArrayLikeBoolNested = ArrayLike # TODO: wait for support for recursive types
# Integers and booleans can generally be used interchangeably
_ArrayLikeIntOrBool = Union[
_IntOrBool,
ndarray,
Sequence[_IntOrBool],
Sequence[_ArrayLikeIntNested],
Sequence[_ArrayLikeBoolNested],
]
# The signature of take() follows a common theme with its overloads:
# 1. A generic comes in; the same generic comes out
# 2. A scalar comes in; a generic comes out
# 3. An array-like object comes in; some keyword ensures that a generic comes out
# 4. An array-like object comes in; an ndarray or generic comes out
@overload
def take(
a: _ScalarGenericDT,
indices: int,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> _ScalarGenericDT: ...
@overload
def take(
a: _Scalar,
indices: int,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> _ScalarNumpy: ...
@overload
def take(
a: ArrayLike,
indices: int,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> _ScalarNumpy: ...
@overload
def take(
a: ArrayLike,
indices: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> Union[_ScalarNumpy, ndarray]: ...
def reshape(a: ArrayLike, newshape: _ShapeLike, order: _Order = ...) -> ndarray: ...
@overload
def choose(
a: _ScalarIntOrBool,
choices: Union[Sequence[ArrayLike], ndarray],
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> _ScalarIntOrBool: ...
@overload
def choose(
a: _IntOrBool,
choices: Union[Sequence[ArrayLike], ndarray],
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> Union[integer, bool_]: ...
@overload
def choose(
a: _ArrayLikeIntOrBool,
choices: Union[Sequence[ArrayLike], ndarray],
out: Optional[ndarray] = ...,
mode: _Mode = ...,
) -> ndarray: ...
def repeat(
a: ArrayLike, repeats: _ArrayLikeIntOrBool, axis: Optional[int] = ...
) -> ndarray: ...
def put(
a: ndarray, ind: _ArrayLikeIntOrBool, v: ArrayLike, mode: _Mode = ...
) -> None: ...
def swapaxes(
a: Union[Sequence[ArrayLike], ndarray], axis1: int, axis2: int
) -> ndarray: ...
def transpose(
a: ArrayLike, axes: Union[None, Sequence[int], ndarray] = ...
) -> ndarray: ...
def partition(
a: ArrayLike,
kth: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
@overload
def argpartition(
a: generic,
kth: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> integer: ...
@overload
def argpartition(
a: _ScalarBuiltin,
kth: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
@overload
def argpartition(
a: ArrayLike,
kth: _ArrayLikeIntOrBool,
axis: Optional[int] = ...,
kind: _PartitionKind = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
def sort(
a: Union[Sequence[ArrayLike], ndarray],
axis: Optional[int] = ...,
kind: Optional[_SortKind] = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
def argsort(
a: Union[Sequence[ArrayLike], ndarray],
axis: Optional[int] = ...,
kind: Optional[_SortKind] = ...,
order: Union[None, str, Sequence[str]] = ...,
) -> ndarray: ...
@overload
def argmax(
a: Union[Sequence[ArrayLike], ndarray],
axis: None = ...,
out: Optional[ndarray] = ...,
) -> integer: ...
@overload
def argmax(
a: Union[Sequence[ArrayLike], ndarray],
axis: int = ...,
out: Optional[ndarray] = ...,
) -> Union[integer, ndarray]: ...
@overload
def argmin(
a: Union[Sequence[ArrayLike], ndarray],
axis: None = ...,
out: Optional[ndarray] = ...,
) -> integer: ...
@overload
def argmin(
a: Union[Sequence[ArrayLike], ndarray],
axis: int = ...,
out: Optional[ndarray] = ...,
) -> Union[integer, ndarray]: ...
@overload
def searchsorted(
a: Union[Sequence[ArrayLike], ndarray],
v: _Scalar,
side: _Side = ...,
sorter: Union[None, Sequence[_IntOrBool], ndarray] = ..., # 1D int array
) -> integer: ...
@overload
def searchsorted(
a: Union[Sequence[ArrayLike], ndarray],
v: ArrayLike,
side: _Side = ...,
sorter: Union[None, Sequence[_IntOrBool], ndarray] = ..., # 1D int array
) -> ndarray: ...
def resize(a: ArrayLike, new_shape: _ShapeLike) -> ndarray: ...
@overload
def squeeze(a: _ScalarGeneric, axis: Optional[_ShapeLike] = ...) -> _ScalarGeneric: ...
@overload
def squeeze(a: ArrayLike, axis: Optional[_ShapeLike] = ...) -> ndarray: ...
def diagonal(
a: Union[Sequence[Sequence[ArrayLike]], ndarray], # >= 2D array
offset: int = ...,
axis1: int = ...,
axis2: int = ...,
) -> ndarray: ...
def trace(
a: Union[Sequence[Sequence[ArrayLike]], ndarray], # >= 2D array
offset: int = ...,
axis1: int = ...,
axis2: int = ...,
dtype: DtypeLike = ...,
out: Optional[ndarray] = ...,
) -> Union[number, ndarray]: ...
def ravel(a: ArrayLike, order: _Order = ...) -> ndarray: ...
def nonzero(a: ArrayLike) -> Tuple[ndarray, ...]: ...
def shape(a: ArrayLike) -> _Shape: ...
def compress(
condition: Union[Sequence[_Bool], ndarray], # 1D bool array
a: ArrayLike,
axis: Optional[int] = ...,
out: Optional[ndarray] = ...,
) -> ndarray: ...
| 0 |
public_repos/numpy-stubs | public_repos/numpy-stubs/numpy-stubs/typing.pyi | import sys
from typing import Any, Dict, List, overload, Sequence, Text, Tuple, Union
from numpy import dtype, ndarray
if sys.version_info >= (3, 8):
from typing import Protocol
else:
from typing_extensions import Protocol
_Shape = Tuple[int, ...]
# Anything that can be coerced to a shape tuple
_ShapeLike = Union[int, Sequence[int]]
_DtypeLikeNested = Any # TODO: wait for support for recursive types
# Anything that can be coerced into numpy.dtype.
# Reference: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
DtypeLike = Union[
dtype,
# default data type (float64)
None,
# array-scalar types and generic types
type, # TODO: enumerate these when we add type hints for numpy scalars
# TODO: add a protocol for anything with a dtype attribute
# character codes, type strings or comma-separated fields, e.g., 'float64'
str,
# (flexible_dtype, itemsize)
Tuple[_DtypeLikeNested, int],
# (fixed_dtype, shape)
Tuple[_DtypeLikeNested, _ShapeLike],
# [(field_name, field_dtype, field_shape), ...]
#
# The type here is quite broad because NumPy accepts quite a wide
# range of inputs inside the list; see the tests for some
# examples.
List[Any],
# {'names': ..., 'formats': ..., 'offsets': ..., 'titles': ...,
# 'itemsize': ...}
# TODO: use TypedDict when/if it's officially supported
Dict[
str,
Union[
Sequence[str], # names
Sequence[_DtypeLikeNested], # formats
Sequence[int], # offsets
Sequence[Union[bytes, Text, None]], # titles
int, # itemsize
],
],
# {'field1': ..., 'field2': ..., ...}
Dict[str, Tuple[_DtypeLikeNested, int]],
# (base_dtype, new_dtype)
Tuple[_DtypeLikeNested, _DtypeLikeNested],
]
class _SupportsArray(Protocol):
@overload
def __array__(self, __dtype: DtypeLike = ...) -> ndarray: ...
@overload
def __array__(self, dtype: DtypeLike = ...) -> ndarray: ...
ArrayLike = Union[bool, int, float, complex, _SupportsArray, Sequence]
| 0 |
public_repos/numpy-stubs/numpy-stubs | public_repos/numpy-stubs/numpy-stubs/core/_internal.pyi | from typing import Any
# TODO: add better annotations when ctypes is stubbed out
class _ctypes:
@property
def data(self) -> int: ...
@property
def shape(self) -> Any: ...
@property
def strides(self) -> Any: ...
def data_as(self, obj: Any) -> Any: ...
def shape_as(self, obj: Any) -> Any: ...
def strides_as(self, obj: Any) -> Any: ...
def get_data(self) -> int: ...
def get_shape(self) -> Any: ...
def get_strides(self) -> Any: ...
def get_as_parameter(self) -> Any: ...
| 0 |
public_repos/numpy-stubs | public_repos/numpy-stubs/tests/test_stubs.py | import importlib.util
import itertools
import os
import re
from collections import defaultdict
import pytest
from mypy import api
TESTS_DIR = os.path.dirname(__file__)
PASS_DIR = os.path.join(TESTS_DIR, "pass")
FAIL_DIR = os.path.join(TESTS_DIR, "fail")
REVEAL_DIR = os.path.join(TESTS_DIR, "reveal")
def get_test_cases(directory):
for root, _, files in os.walk(directory):
for fname in files:
if os.path.splitext(fname)[-1] == ".py":
fullpath = os.path.join(root, fname)
# Use relative path for nice py.test name
relpath = os.path.relpath(fullpath, start=directory)
yield pytest.param(
fullpath,
# Manually specify a name for the test
id=relpath,
)
@pytest.mark.parametrize("path", get_test_cases(PASS_DIR))
def test_success(path):
stdout, stderr, exitcode = api.run([path])
assert exitcode == 0, stdout
assert re.match(r"Success: no issues found in \d+ source files?", stdout.strip())
@pytest.mark.parametrize("path", get_test_cases(FAIL_DIR))
def test_fail(path):
stdout, stderr, exitcode = api.run([path])
assert exitcode != 0
with open(path) as fin:
lines = fin.readlines()
errors = defaultdict(lambda: "")
error_lines = stdout.rstrip("\n").split("\n")
assert re.match(
r"Found \d+ errors? in \d+ files? \(checked \d+ source files?\)",
error_lines[-1].strip(),
)
for error_line in error_lines[:-1]:
error_line = error_line.strip()
if not error_line:
continue
lineno = int(error_line.split(":")[1])
errors[lineno] += error_line
for i, line in enumerate(lines):
lineno = i + 1
if " E:" not in line and lineno not in errors:
continue
target_line = lines[lineno - 1]
if "# E:" in target_line:
marker = target_line.split("# E:")[-1].strip()
assert lineno in errors, f'Extra error "{marker}"'
assert marker in errors[lineno]
else:
pytest.fail(f"Error {repr(errors[lineno])} not found")
@pytest.mark.parametrize("path", get_test_cases(REVEAL_DIR))
def test_reveal(path):
stdout, stderr, exitcode = api.run([path])
with open(path) as fin:
lines = fin.readlines()
for error_line in stdout.split("\n"):
error_line = error_line.strip()
if not error_line:
continue
lineno = int(error_line.split(":")[1])
assert "Revealed type is" in error_line
marker = lines[lineno - 1].split("# E:")[-1].strip()
assert marker in error_line
@pytest.mark.parametrize("path", get_test_cases(PASS_DIR))
def test_code_runs(path):
path_without_extension, _ = os.path.splitext(path)
dirname, filename = path.split(os.sep)[-2:]
spec = importlib.util.spec_from_file_location(f"{dirname}.{filename}", path)
test_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(test_module)
| 0 |
public_repos/numpy-stubs | public_repos/numpy-stubs/tests/README.md | Testing
=======
There are three main directories of tests right now:
- `pass/` which contain Python files that must pass `mypy` checking with
no type errors
- `fail/` which contain Python files that must *fail* `mypy` checking
with the annotated errors
- `reveal/` which contain Python files that must output the correct
types with `reveal_type`
`fail` and `reveal` are annotated with comments that specify what error
`mypy` threw and what type should be revealed respectively. The format
looks like:
```python
bad_function # E: <error message>
reveal_type(x) # E: <type name>
```
Right now, the error messages and types are must be **contained within
corresponding mypy message**.
## Running the tests
To setup your test environment, cd into the root of the repo and run:
```
pip install -r test-requirements.txt
```
To run the tests, do
```
python runtests.py
```
from the repo root. To run `mypy` on a specific file (which can be
useful for debugging), you can also run:
```
pip install . # Make sure stubs are installed; runtests does this for you
mypy <file_path>
```
Note that it is assumed that all of these commands target the same
underlying Python interpreter. To ensure you're using the intended version of
Python you can use `python -m` versions of these commands instead:
```
python -m pip install -r test-requirements.txt
python -m pip install .
python -m mypy <file_path>
```
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/fail/numerictypes.py | import numpy as np
# Techincally this works, but probably shouldn't. See
#
# https://github.com/numpy/numpy/issues/16366
#
np.maximum_sctype(1) # E: incompatible type "int"
np.issubsctype(1, np.int64) # E: incompatible type "int"
np.issubdtype(1, np.int64) # E: incompatible type "int"
np.find_common_type(np.int64, np.int64) # E: incompatible type "Type[int64]"
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/fail/scalars.py | import numpy as np
# Construction
np.float32(3j) # E: incompatible type
# Technically the following examples are valid NumPy code. But they
# are not considered a best practice, and people who wish to use the
# stubs should instead do
#
# np.array([1.0, 0.0, 0.0], dtype=np.float32)
# np.array([], dtype=np.complex64)
#
# See e.g. the discussion on the mailing list
#
# https://mail.python.org/pipermail/numpy-discussion/2020-April/080566.html
#
# and the issue
#
# https://github.com/numpy/numpy-stubs/issues/41
#
# for more context.
np.float32([1.0, 0.0, 0.0]) # E: incompatible type
np.complex64([]) # E: incompatible type
np.complex64(1, 2) # E: Too many arguments
# TODO: protocols (can't check for non-existent protocols w/ __getattr__)
np.datetime64(0) # E: non-matching overload
dt_64 = np.datetime64(0, "D")
td_64 = np.timedelta64(1, "h")
dt_64 + dt_64 # E: Unsupported operand types
td_64 - dt_64 # E: Unsupported operand types
td_64 / dt_64 # E: No overload
td_64 % 1 # E: Unsupported operand types
td_64 % dt_64 # E: Unsupported operand types
class A:
def __float__(self):
return 1.0
np.int8(A()) # E: incompatible type
np.int16(A()) # E: incompatible type
np.int32(A()) # E: incompatible type
np.int64(A()) # E: incompatible type
np.uint8(A()) # E: incompatible type
np.uint16(A()) # E: incompatible type
np.uint32(A()) # E: incompatible type
np.uint64(A()) # E: incompatible type
np.void("test") # E: incompatible type
np.generic(1) # E: Cannot instantiate abstract class
np.number(1) # E: Cannot instantiate abstract class
np.integer(1) # E: Cannot instantiate abstract class
np.signedinteger(1) # E: Cannot instantiate abstract class
np.unsignedinteger(1) # E: Cannot instantiate abstract class
np.inexact(1) # E: Cannot instantiate abstract class
np.floating(1) # E: Cannot instantiate abstract class
np.complexfloating(1) # E: Cannot instantiate abstract class
np.character("test") # E: Cannot instantiate abstract class
np.flexible(b"test") # E: Cannot instantiate abstract class
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/fail/simple.py | """Simple expression that should fail with mypy."""
import numpy as np
# Array creation routines checks
np.zeros("test") # E: incompatible type
np.zeros() # E: Too few arguments
np.ones("test") # E: incompatible type
np.ones() # E: Too few arguments
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/fail/warnings_and_errors.py | import numpy as np
np.AxisError(1.0) # E: Argument 1 to "AxisError" has incompatible type
np.AxisError(1, ndim=2.0) # E: Argument "ndim" to "AxisError" has incompatible type
np.AxisError(
2, msg_prefix=404 # E: Argument "msg_prefix" to "AxisError" has incompatible type
)
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/fail/ufuncs.py | import numpy as np
np.sin.nin + "foo" # E: Unsupported operand types
np.sin(1, foo="bar") # E: Unexpected keyword argument
np.sin(1, extobj=["foo", "foo", "foo"]) # E: incompatible type
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/fail/fromnumeric.py | """Tests for :mod:`numpy.core.fromnumeric`."""
import numpy as np
A = np.array(True, ndmin=2, dtype=bool)
A.setflags(write=False)
a = np.bool_(True)
np.take(a, None) # E: No overload variant of "take" matches argument type
np.take(a, axis=1.0) # E: No overload variant of "take" matches argument type
np.take(A, out=1) # E: No overload variant of "take" matches argument type
np.take(A, mode="bob") # E: No overload variant of "take" matches argument type
np.reshape(a, None) # E: Argument 2 to "reshape" has incompatible type
np.reshape(A, 1, order="bob") # E: Argument "order" to "reshape" has incompatible type
np.choose(a, None) # E: No overload variant of "choose" matches argument type
np.choose(a, out=1.0) # E: No overload variant of "choose" matches argument type
np.choose(A, mode="bob") # E: No overload variant of "choose" matches argument type
np.repeat(a, None) # E: Argument 2 to "repeat" has incompatible type
np.repeat(A, 1, axis=1.0) # E: Argument "axis" to "repeat" has incompatible type
np.swapaxes(a, 0, 0) # E: Argument 1 to "swapaxes" has incompatible type
np.swapaxes(A, None, 1) # E: Argument 2 to "swapaxes" has incompatible type
np.swapaxes(A, 1, [0]) # E: Argument 3 to "swapaxes" has incompatible type
np.transpose(a, axes=1) # E: Argument "axes" to "transpose" has incompatible type
np.transpose(A, axes=1.0) # E: Argument "axes" to "transpose" has incompatible type
np.partition(a, None) # E: Argument 2 to "partition" has incompatible type
np.partition(
a, 0, axis="bob" # E: Argument "axis" to "partition" has incompatible type
)
np.partition(
A, 0, kind="bob" # E: Argument "kind" to "partition" has incompatible type
)
np.partition(
A, 0, order=range(5) # E: Argument "order" to "partition" has incompatible type
)
np.argpartition( # E: No overload variant of "argpartition" matches argument type
a, None
)
np.argpartition( # E: No overload variant of "argpartition" matches argument type
a, 0, axis="bob"
)
np.argpartition( # E: No overload variant of "argpartition" matches argument type
A, 0, kind="bob"
)
np.argpartition(
A, 0, order=range(5) # E: Argument "order" to "argpartition" has incompatible type
)
np.sort(a) # E: Argument 1 to "sort" has incompatible type
np.sort(A, axis="bob") # E: Argument "axis" to "sort" has incompatible type
np.sort(A, kind="bob") # E: Argument "kind" to "sort" has incompatible type
np.sort(A, order=range(5)) # E: Argument "order" to "sort" has incompatible type
np.argsort(a) # E: Argument 1 to "argsort" has incompatible type
np.argsort(A, axis="bob") # E: Argument "axis" to "argsort" has incompatible type
np.argsort(A, kind="bob") # E: Argument "kind" to "argsort" has incompatible type
np.argsort(A, order=range(5)) # E: Argument "order" to "argsort" has incompatible type
np.argmax(a) # E: No overload variant of "argmax" matches argument type
np.argmax(A, axis="bob") # E: No overload variant of "argmax" matches argument type
np.argmax(A, kind="bob") # E: No overload variant of "argmax" matches argument type
np.argmin(a) # E: No overload variant of "argmin" matches argument type
np.argmin(A, axis="bob") # E: No overload variant of "argmin" matches argument type
np.argmin(A, kind="bob") # E: No overload variant of "argmin" matches argument type
np.searchsorted(a, 0) # E: No overload variant of "searchsorted" matches argument type
np.searchsorted( # E: No overload variant of "searchsorted" matches argument type
A[0], 0, side="bob"
)
np.searchsorted( # E: No overload variant of "searchsorted" matches argument type
A[0], 0, sorter=1.0
)
np.resize(A, 1.0) # E: Argument 2 to "resize" has incompatible type
np.squeeze(A, 1.0) # E: No overload variant of "squeeze" matches argument type
np.diagonal(a) # E: Argument 1 to "diagonal" has incompatible type
np.diagonal(A, offset=None) # E: Argument "offset" to "diagonal" has incompatible type
np.diagonal(A, axis1="bob") # E: Argument "axis1" to "diagonal" has incompatible type
np.diagonal(A, axis2=[]) # E: Argument "axis2" to "diagonal" has incompatible type
np.trace(a) # E: Argument 1 to "trace" has incompatible type
np.trace(A, offset=None) # E: Argument "offset" to "trace" has incompatible type
np.trace(A, axis1="bob") # E: Argument "axis1" to "trace" has incompatible type
np.trace(A, axis2=[]) # E: Argument "axis2" to "trace" has incompatible type
np.ravel(a, order="bob") # E: Argument "order" to "ravel" has incompatible type
np.compress(True, A) # E: Argument 1 to "compress" has incompatible type
np.compress(
[True], A, axis=1.0 # E: Argument "axis" to "compress" has incompatible type
)
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/fail/ndarray.py | import numpy as np
# Ban setting dtype since mutating the type of the array in place
# makes having ndarray be generic over dtype impossible. Generally
# users should use `ndarray.view` in this situation anyway. See
#
# https://github.com/numpy/numpy-stubs/issues/7
#
# for more context.
float_array = np.array([1.0])
float_array.dtype = np.bool_ # E: Property "dtype" defined in "ndarray" is read-only
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/fail/array_like.py | from typing import Any, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from numpy.typing import ArrayLike
else:
ArrayLike = Any
class A:
pass
x1: ArrayLike = (i for i in range(10)) # E: Incompatible types in assignment
x2: ArrayLike = A() # E: Incompatible types in assignment
x3: ArrayLike = {1: "foo", 2: "bar"} # E: Incompatible types in assignment
scalar = np.int64(1)
scalar.__array__(dtype=np.float64) # E: Unexpected keyword argument
array = np.array([1])
array.__array__(dtype=np.float64) # E: Unexpected keyword argument
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/numerictypes.py | import numpy as np
np.maximum_sctype("S8")
np.maximum_sctype(object)
np.issctype(object)
np.issctype("S8")
np.obj2sctype(list)
np.obj2sctype(list, default=None)
np.obj2sctype(list, default=np.string_)
np.issubclass_(np.int32, int)
np.issubclass_(np.float64, float)
np.issubclass_(np.float64, (int, float))
np.issubsctype("int64", int)
np.issubsctype(np.array([1]), np.array([1]))
np.issubdtype("S1", np.string_)
np.issubdtype(np.float64, np.float32)
np.sctype2char("S1")
np.sctype2char(list)
np.find_common_type([], [np.int64, np.float32, complex])
np.find_common_type((), (np.int64, np.float32, complex))
np.find_common_type([np.int64, np.float32], [])
np.find_common_type([np.float32], [np.int64, np.float64])
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/scalars.py | import numpy as np
# Construction
class C:
def __complex__(self):
return 3j
class B:
def __int__(self):
return 4
class A:
def __float__(self):
return 4.0
np.complex64(3j)
np.complex64(C())
np.complex128(3j)
np.complex128(C())
np.int8(4)
np.int16(3.4)
np.int32(4)
np.int64(-1)
np.uint8(B())
np.uint32()
np.float16(A())
np.float32(16)
np.float64(3.0)
np.bytes_(b"hello")
np.str_("hello")
# Protocols
float(np.int8(4))
int(np.int16(5))
np.int8(np.float32(6))
# TODO(alan): test after https://github.com/python/typeshed/pull/2004
# complex(np.int32(8))
abs(np.int8(4))
# Array-ish semantics
np.int8().real
np.int16().imag
np.int32().data
np.int64().flags
np.uint8().itemsize * 2
np.uint16().ndim + 1
np.uint32().strides
np.uint64().shape
# Time structures
np.datetime64()
np.datetime64(0, "D")
np.datetime64("2019")
np.datetime64("2019", "D")
np.timedelta64()
np.timedelta64(0)
np.timedelta64(0, "D")
dt_64 = np.datetime64(0, "D")
td_64 = np.timedelta64(1, "h")
dt_64 + td_64
dt_64 - dt_64
dt_64 - td_64
td_64 + td_64
td_64 - td_64
td_64 / 1.0
td_64 / td_64
td_64 % td_64
np.void(1)
np.void(np.int64(1))
np.void(True)
np.void(np.bool_(True))
np.void(b"test")
np.void(np.bytes_("test"))
| 0 |
public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/simple.py | """Simple expression that should pass with mypy."""
import operator
import numpy as np
from typing import Iterable # noqa: F401
# Basic checks
array = np.array([1, 2])
def ndarray_func(x):
# type: (np.ndarray) -> np.ndarray
return x
ndarray_func(np.array([1, 2]))
array == 1
array.dtype == float
# Array creation routines checks
ndarray_func(np.zeros([1, 2]))
ndarray_func(np.ones([1, 2]))
ndarray_func(np.empty([1, 2]))
ndarray_func(np.zeros_like(array))
ndarray_func(np.ones_like(array))
ndarray_func(np.empty_like(array))
# Dtype construction
np.dtype(float)
np.dtype(np.float64)
np.dtype(None)
np.dtype("float64")
np.dtype(np.dtype(float))
np.dtype(("U", 10))
np.dtype((np.int32, (2, 2)))
# Define the arguments on the previous line to prevent bidirectional
# type inference in mypy from broadening the types.
two_tuples_dtype = [("R", "u1"), ("G", "u1"), ("B", "u1")]
np.dtype(two_tuples_dtype)
three_tuples_dtype = [("R", "u1", 1)]
np.dtype(three_tuples_dtype)
mixed_tuples_dtype = [("R", "u1"), ("G", np.unicode_, 1)]
np.dtype(mixed_tuples_dtype)
shape_tuple_dtype = [("R", "u1", (2, 2))]
np.dtype(shape_tuple_dtype)
shape_like_dtype = [("R", "u1", (2, 2)), ("G", np.unicode_, 1)]
np.dtype(shape_like_dtype)
object_dtype = [("field1", object)]
np.dtype(object_dtype)
np.dtype({"col1": ("U10", 0), "col2": ("float32", 10)})
np.dtype((np.int32, {"real": (np.int16, 0), "imag": (np.int16, 2)}))
np.dtype((np.int32, (np.int8, 4)))
# Dtype comparision
np.dtype(float) == float
np.dtype(float) != np.float64
np.dtype(float) < None
np.dtype(float) <= "float64"
np.dtype(float) > np.dtype(float)
np.dtype(float) >= np.dtype(("U", 10))
# Iteration and indexing
def iterable_func(x):
# type: (Iterable) -> Iterable
return x
iterable_func(array)
[element for element in array]
iter(array)
zip(array, array)
array[1]
array[:]
array[...]
array[:] = 0
array_2d = np.ones((3, 3))
array_2d[:2, :2]
array_2d[..., 0]
array_2d[:2, :2] = 0
# Other special methods
len(array)
str(array)
array_scalar = np.array(1)
int(array_scalar)
float(array_scalar)
# currently does not work due to https://github.com/python/typeshed/issues/1904
# complex(array_scalar)
bytes(array_scalar)
operator.index(array_scalar)
bool(array_scalar)
# comparisons
array < 1
array <= 1
array == 1
array != 1
array > 1
array >= 1
1 < array
1 <= array
1 == array
1 != array
1 > array
1 >= array
# binary arithmetic
array + 1
1 + array
array += 1
array - 1
1 - array
array -= 1
array * 1
1 * array
array *= 1
array / 1
1 / array
float_array = np.array([1.0, 2.0])
float_array /= 1
array // 1
1 // array
array //= 1
array % 1
1 % array
array %= 1
divmod(array, 1)
divmod(1, array)
array ** 1
1 ** array
array **= 1
array << 1
1 << array
array <<= 1
array >> 1
1 >> array
array >>= 1
array & 1
1 & array
array &= 1
array ^ 1
1 ^ array
array ^= 1
array | 1
1 | array
array |= 1
# unary arithmetic
-array
+array
abs(array)
~array
# Other methods
np.array([1, 2]).transpose()
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public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/warnings_and_errors.py | import numpy as np
np.AxisError(1)
np.AxisError(1, ndim=2)
np.AxisError(1, ndim=None)
np.AxisError(1, ndim=2, msg_prefix="error")
np.AxisError(1, ndim=2, msg_prefix=None)
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public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/ufuncs.py | import numpy as np
np.sin(1)
np.sin([1, 2, 3])
np.sin(1, out=np.empty(1))
np.matmul(np.ones((2, 2, 2)), np.ones((2, 2, 2)), axes=[(0, 1), (0, 1), (0, 1)])
np.sin(1, signature="D")
np.sin(1, extobj=[16, 1, lambda: None])
np.sin(1) + np.sin(1)
np.sin.types[0]
np.sin.__name__
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public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/fromnumeric.py | """Tests for :mod:`numpy.core.fromnumeric`."""
import numpy as np
A = np.array(True, ndmin=2, dtype=bool)
B = np.array(1.0, ndmin=2, dtype=np.float32)
A.setflags(write=False)
B.setflags(write=False)
a = np.bool_(True)
b = np.float32(1.0)
c = 1.0
np.take(a, 0)
np.take(b, 0)
np.take(c, 0)
np.take(A, 0)
np.take(B, 0)
np.take(A, [0])
np.take(B, [0])
np.reshape(a, 1)
np.reshape(b, 1)
np.reshape(c, 1)
np.reshape(A, 1)
np.reshape(B, 1)
np.choose(a, [True, True])
np.choose(A, [1.0, 1.0])
np.repeat(a, 1)
np.repeat(b, 1)
np.repeat(c, 1)
np.repeat(A, 1)
np.repeat(B, 1)
np.swapaxes(A, 0, 0)
np.swapaxes(B, 0, 0)
np.transpose(a)
np.transpose(b)
np.transpose(c)
np.transpose(A)
np.transpose(B)
np.partition(a, 0, axis=None)
np.partition(b, 0, axis=None)
np.partition(c, 0, axis=None)
np.partition(A, 0)
np.partition(B, 0)
np.argpartition(a, 0)
np.argpartition(b, 0)
np.argpartition(c, 0)
np.argpartition(A, 0)
np.argpartition(B, 0)
np.sort(A, 0)
np.sort(B, 0)
np.argsort(A, 0)
np.argsort(B, 0)
np.argmax(A)
np.argmax(B)
np.argmax(A, axis=0)
np.argmax(B, axis=0)
np.argmin(A)
np.argmin(B)
np.argmin(A, axis=0)
np.argmin(B, axis=0)
np.searchsorted(A[0], 0)
np.searchsorted(B[0], 0)
np.searchsorted(A[0], [0])
np.searchsorted(B[0], [0])
np.resize(a, (5, 5))
np.resize(b, (5, 5))
np.resize(c, (5, 5))
np.resize(A, (5, 5))
np.resize(B, (5, 5))
np.squeeze(a)
np.squeeze(b)
np.squeeze(c)
np.squeeze(A)
np.squeeze(B)
np.diagonal(A)
np.diagonal(B)
np.trace(A)
np.trace(B)
np.ravel(a)
np.ravel(b)
np.ravel(c)
np.ravel(A)
np.ravel(B)
np.nonzero(a)
np.nonzero(b)
np.nonzero(c)
np.nonzero(A)
np.nonzero(B)
np.shape(a)
np.shape(b)
np.shape(c)
np.shape(A)
np.shape(B)
np.compress([True], a)
np.compress([True], b)
np.compress([True], c)
np.compress([True], A)
np.compress([True], B)
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public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/simple_py3.py | import numpy as np
array = np.array([1, 2])
# The @ operator is not in python 2
array @ array
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public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/ndarray_shape_manipulation.py | import numpy as np
nd1 = np.array([[1, 2], [3, 4]])
# reshape
nd1.reshape(4)
nd1.reshape(2, 2)
nd1.reshape((2, 2))
nd1.reshape((2, 2), order="C")
nd1.reshape(4, order="C")
# resize
nd1.resize()
nd1.resize(4)
nd1.resize(2, 2)
nd1.resize((2, 2))
nd1.resize((2, 2), refcheck=True)
nd1.resize(4, refcheck=True)
nd2 = np.array([[1, 2], [3, 4]])
# transpose
nd2.transpose()
nd2.transpose(1, 0)
nd2.transpose((1, 0))
# swapaxes
nd2.swapaxes(0, 1)
# flatten
nd2.flatten()
nd2.flatten("C")
# ravel
nd2.ravel()
nd2.ravel("C")
# squeeze
nd2.squeeze()
nd3 = np.array([[1, 2]])
nd3.squeeze(0)
nd4 = np.array([[[1, 2]]])
nd4.squeeze((0, 1))
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public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/ndarray_conversion.py | import os
import tempfile
import numpy as np
nd = np.array([[1, 2], [3, 4]])
scalar_array = np.array(1)
# item
scalar_array.item()
nd.item(1)
nd.item(0, 1)
nd.item((0, 1))
# tolist is pretty simple
# itemset
scalar_array.itemset(3)
nd.itemset(3, 0)
nd.itemset((0, 0), 3)
# tostring/tobytes
nd.tostring()
nd.tostring("C")
nd.tostring(None)
nd.tobytes()
nd.tobytes("C")
nd.tobytes(None)
# tofile
if os.name != "nt":
with tempfile.NamedTemporaryFile(suffix=".txt") as tmp:
nd.tofile(tmp.name)
nd.tofile(tmp.name, "")
nd.tofile(tmp.name, sep="")
nd.tofile(tmp.name, "", "%s")
nd.tofile(tmp.name, format="%s")
nd.tofile(tmp)
# dump is pretty simple
# dumps is pretty simple
# astype
nd.astype("float")
nd.astype(float)
nd.astype(float, "K")
nd.astype(float, order="K")
nd.astype(float, "K", "unsafe")
nd.astype(float, casting="unsafe")
nd.astype(float, "K", "unsafe", True)
nd.astype(float, subok=True)
nd.astype(float, "K", "unsafe", True, True)
nd.astype(float, copy=True)
# byteswap
nd.byteswap()
nd.byteswap(True)
# copy
nd.copy()
nd.copy("C")
# view
nd.view()
nd.view(np.int64)
nd.view(dtype=np.int64)
nd.view(np.int64, np.matrix)
nd.view(type=np.matrix)
# getfield
complex_array = np.array([[1 + 1j, 0], [0, 1 - 1j]], dtype=np.complex128)
complex_array.getfield("float")
complex_array.getfield(float)
complex_array.getfield("float", 8)
complex_array.getfield(float, offset=8)
# setflags
nd.setflags()
nd.setflags(True)
nd.setflags(write=True)
nd.setflags(True, True)
nd.setflags(write=True, align=True)
nd.setflags(True, True, False)
nd.setflags(write=True, align=True, uic=False)
# fill is pretty simple
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public_repos/numpy-stubs/tests | public_repos/numpy-stubs/tests/pass/array_like.py | from typing import Any, List, Optional, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from numpy.typing import ArrayLike, DtypeLike, _SupportsArray
else:
ArrayLike = Any
DtypeLike = Any
_SupportsArray = Any
x1: ArrayLike = True
x2: ArrayLike = 5
x3: ArrayLike = 1.0
x4: ArrayLike = 1 + 1j
x5: ArrayLike = np.int8(1)
x6: ArrayLike = np.float64(1)
x7: ArrayLike = np.complex128(1)
x8: ArrayLike = np.array([1, 2, 3])
x9: ArrayLike = [1, 2, 3]
x10: ArrayLike = (1, 2, 3)
x11: ArrayLike = "foo"
class A:
def __array__(self, dtype: DtypeLike = None) -> np.ndarray:
return np.array([1, 2, 3])
x12: ArrayLike = A()
scalar: _SupportsArray = np.int64(1)
scalar.__array__(np.float64)
array: _SupportsArray = np.array(1)
array.__array__(np.float64)
a: _SupportsArray = A()
a.__array__(np.int64)
a.__array__(dtype=np.int64)
# Escape hatch for when you mean to make something like an object
# array.
object_array_scalar: Any = (i for i in range(10))
np.array(object_array_scalar)
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